Overview

Brought to you by YData

Dataset statistics

Number of variables66
Number of observations182242
Missing cells3131080
Missing cells (%)26.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.8 MiB
Average record size in memory528.0 B

Variable types

Numeric23
Text14
Categorical27
Boolean1
Unsupported1

Alerts

BLDG_SEQ has constant value "1"Constant
SFYI_VALUE has constant value "0"Constant
AC_TYPE is highly overall correlated with COM_UNITS and 4 other fieldsHigh correlation
BDRM_COND is highly overall correlated with GROSS_AREA and 3 other fieldsHigh correlation
BED_RMS is highly overall correlated with FULL_BTH and 5 other fieldsHigh correlation
BTHRM_STYLE1 is highly overall correlated with BTHRM_STYLE2 and 8 other fieldsHigh correlation
BTHRM_STYLE2 is highly overall correlated with BTHRM_STYLE1 and 7 other fieldsHigh correlation
BTHRM_STYLE3 is highly overall correlated with BTHRM_STYLE1 and 9 other fieldsHigh correlation
CD_FLOOR is highly overall correlated with KITCHEN_STYLE2 and 2 other fieldsHigh correlation
CITY is highly overall correlated with CM_ID and 4 other fieldsHigh correlation
CM_ID is highly overall correlated with CITY and 5 other fieldsHigh correlation
COM_UNITS is highly overall correlated with AC_TYPE and 6 other fieldsHigh correlation
CORNER_UNIT is highly overall correlated with GROSS_AREA and 5 other fieldsHigh correlation
EXT_FNISHED is highly overall correlated with NUM_PARKING and 1 other fieldsHigh correlation
FULL_BTH is highly overall correlated with BED_RMS and 2 other fieldsHigh correlation
GIS_ID is highly overall correlated with CITY and 4 other fieldsHigh correlation
GROSS_AREA is highly overall correlated with AC_TYPE and 19 other fieldsHigh correlation
HEAT_SYSTEM is highly overall correlated with COM_UNITS and 5 other fieldsHigh correlation
HEAT_TYPE is highly overall correlated with COM_UNITS and 4 other fieldsHigh correlation
INT_COND is highly overall correlated with BTHRM_STYLE1 and 4 other fieldsHigh correlation
INT_WALL is highly overall correlated with GROSS_AREA and 1 other fieldsHigh correlation
KITCHENS is highly overall correlated with BED_RMS and 4 other fieldsHigh correlation
KITCHEN_STYLE1 is highly overall correlated with BTHRM_STYLE1 and 8 other fieldsHigh correlation
KITCHEN_STYLE2 is highly overall correlated with BTHRM_STYLE1 and 11 other fieldsHigh correlation
KITCHEN_STYLE3 is highly overall correlated with BDRM_COND and 13 other fieldsHigh correlation
KITCHEN_TYPE is highly overall correlated with GROSS_AREA and 4 other fieldsHigh correlation
LIVING_AREA is highly overall correlated with AC_TYPE and 18 other fieldsHigh correlation
LU is highly overall correlated with CD_FLOOR and 9 other fieldsHigh correlation
LUC is highly overall correlated with BTHRM_STYLE1 and 9 other fieldsHigh correlation
NUM_PARKING is highly overall correlated with BDRM_COND and 5 other fieldsHigh correlation
ORIENTATION is highly overall correlated with GROSS_AREA and 5 other fieldsHigh correlation
OWN_OCC is highly overall correlated with COM_UNITS and 3 other fieldsHigh correlation
PID is highly overall correlated with CITY and 4 other fieldsHigh correlation
PROP_VIEW is highly overall correlated with COM_UNITS and 1 other fieldsHigh correlation
RC_UNITS is highly overall correlated with AC_TYPE and 6 other fieldsHigh correlation
RES_FLOOR is highly overall correlated with BED_RMS and 5 other fieldsHigh correlation
RES_UNITS is highly overall correlated with AC_TYPE and 4 other fieldsHigh correlation
ROOF_COVER is highly overall correlated with ROOF_STRUCTUREHigh correlation
ROOF_STRUCTURE is highly overall correlated with ROOF_COVERHigh correlation
STRUCTURE_CLASS is highly overall correlated with COM_UNITS and 3 other fieldsHigh correlation
TT_RMS is highly overall correlated with BED_RMS and 5 other fieldsHigh correlation
YR_BUILT is highly overall correlated with BTHRM_STYLE3 and 3 other fieldsHigh correlation
ZIP_CODE is highly overall correlated with CITY and 4 other fieldsHigh correlation
_id is highly overall correlated with CITY and 4 other fieldsHigh correlation
NUM_BLDGS is highly imbalanced (99.9%)Imbalance
INT_WALL is highly imbalanced (88.5%)Imbalance
OVERALL_COND is highly imbalanced (69.8%)Imbalance
BDRM_COND is highly imbalanced (61.9%)Imbalance
PROP_VIEW is highly imbalanced (62.3%)Imbalance
CM_ID has 88951 (48.8%) missing valuesMissing
ST_NUM has 9363 (5.1%) missing valuesMissing
UNIT_NUM has 99629 (54.7%) missing valuesMissing
BLDG_TYPE has 2616 (1.4%) missing valuesMissing
MAIL_ADDRESSEE has 147830 (81.1%) missing valuesMissing
RES_FLOOR has 33792 (18.5%) missing valuesMissing
CD_FLOOR has 110270 (60.5%) missing valuesMissing
RES_UNITS has 171474 (94.1%) missing valuesMissing
COM_UNITS has 171474 (94.1%) missing valuesMissing
RC_UNITS has 171474 (94.1%) missing valuesMissing
LAND_SF has 8002 (4.4%) missing valuesMissing
GROSS_AREA has 33848 (18.6%) missing valuesMissing
LIVING_AREA has 34141 (18.7%) missing valuesMissing
YR_BUILT has 22786 (12.5%) missing valuesMissing
YR_REMODEL has 95524 (52.4%) missing valuesMissing
STRUCTURE_CLASS has 164836 (90.4%) missing valuesMissing
ROOF_STRUCTURE has 36225 (19.9%) missing valuesMissing
ROOF_COVER has 36219 (19.9%) missing valuesMissing
INT_WALL has 48749 (26.7%) missing valuesMissing
EXT_FNISHED has 22884 (12.6%) missing valuesMissing
INT_COND has 48746 (26.7%) missing valuesMissing
EXT_COND has 36158 (19.8%) missing valuesMissing
OVERALL_COND has 9587 (5.3%) missing valuesMissing
BED_RMS has 48765 (26.8%) missing valuesMissing
FULL_BTH has 11644 (6.4%) missing valuesMissing
HLF_BTH has 11509 (6.3%) missing valuesMissing
KITCHENS has 11718 (6.4%) missing valuesMissing
TT_RMS has 48829 (26.8%) missing valuesMissing
BDRM_COND has 110500 (60.6%) missing valuesMissing
BTHRM_STYLE1 has 49548 (27.2%) missing valuesMissing
BTHRM_STYLE2 has 97077 (53.3%) missing valuesMissing
BTHRM_STYLE3 has 145740 (80.0%) missing valuesMissing
KITCHEN_TYPE has 49555 (27.2%) missing valuesMissing
KITCHEN_STYLE1 has 49549 (27.2%) missing valuesMissing
KITCHEN_STYLE2 has 150994 (82.9%) missing valuesMissing
KITCHEN_STYLE3 has 168497 (92.5%) missing valuesMissing
HEAT_TYPE has 48242 (26.5%) missing valuesMissing
HEAT_SYSTEM has 110013 (60.4%) missing valuesMissing
AC_TYPE has 48272 (26.5%) missing valuesMissing
FIREPLACES has 49534 (27.2%) missing valuesMissing
ORIENTATION has 110268 (60.5%) missing valuesMissing
NUM_PARKING has 48623 (26.7%) missing valuesMissing
PROP_VIEW has 46953 (25.8%) missing valuesMissing
CORNER_UNIT has 110271 (60.5%) missing valuesMissing
COM_UNITS is highly skewed (γ1 = 61.02897174)Skewed
RC_UNITS is highly skewed (γ1 = 59.88420383)Skewed
GROSS_AREA is highly skewed (γ1 = 55.89596558)Skewed
LIVING_AREA is highly skewed (γ1 = 63.65241616)Skewed
YR_BUILT is highly skewed (γ1 = 146.1067555)Skewed
YR_REMODEL is highly skewed (γ1 = 248.0727809)Skewed
NUM_PARKING is highly skewed (γ1 = 29.67993455)Skewed
_id is uniformly distributedUniform
_id has unique valuesUnique
MAIL_ZIP_CODE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CD_FLOOR has 8077 (4.4%) zerosZeros
COM_UNITS has 10131 (5.6%) zerosZeros
RC_UNITS has 10731 (5.9%) zerosZeros
BED_RMS has 3184 (1.7%) zerosZeros
FULL_BTH has 36940 (20.3%) zerosZeros
HLF_BTH has 135661 (74.4%) zerosZeros
KITCHENS has 36863 (20.2%) zerosZeros
FIREPLACES has 96980 (53.2%) zerosZeros
NUM_PARKING has 58524 (32.1%) zerosZeros

Reproduction

Analysis started2024-09-12 18:37:28.999228
Analysis finished2024-09-12 18:38:49.866347
Duration1 minute and 20.87 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct182242
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91121.5
Minimum1
Maximum182242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:49.917665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9113.05
Q145561.25
median91121.5
Q3136681.75
95-th percentile173129.95
Maximum182242
Range182241
Interquartile range (IQR)91120.5

Descriptive statistics

Standard deviation52608.878
Coefficient of variation (CV)0.57734869
Kurtosis-1.2
Mean91121.5
Median Absolute Deviation (MAD)45560.5
Skewness0
Sum1.6606164 × 1010
Variance2.7676941 × 109
MonotonicityStrictly increasing
2024-09-12T14:38:50.005750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
121466 1
 
< 0.1%
121490 1
 
< 0.1%
121491 1
 
< 0.1%
121492 1
 
< 0.1%
121493 1
 
< 0.1%
121494 1
 
< 0.1%
121495 1
 
< 0.1%
121496 1
 
< 0.1%
121497 1
 
< 0.1%
Other values (182232) 182232
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
182242 1
< 0.1%
182241 1
< 0.1%
182240 1
< 0.1%
182239 1
< 0.1%
182238 1
< 0.1%
182237 1
< 0.1%
182236 1
< 0.1%
182235 1
< 0.1%
182234 1
< 0.1%
182233 1
< 0.1%

PID
Real number (ℝ)

HIGH CORRELATION 

Distinct182235
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1400927 × 109
Minimum1.00001 × 108
Maximum2.20567 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:50.093472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.00001 × 108
5-th percentile1.0619905 × 108
Q15.0158801 × 108
median1.1026175 × 109
Q31.810508 × 109
95-th percentile2.102473 × 109
Maximum2.20567 × 109
Range2.105669 × 109
Interquartile range (IQR)1.30892 × 109

Descriptive statistics

Standard deviation7.0911136 × 108
Coefficient of variation (CV)0.62197691
Kurtosis-1.5581055
Mean1.1400927 × 109
Median Absolute Deviation (MAD)7.0156449 × 108
Skewness0.034817957
Sum2.0777278 × 1014
Variance5.0283892 × 1017
MonotonicityIncreasing
2024-09-12T14:38:50.179191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100710002 2
 
< 0.1%
1404289000 2
 
< 0.1%
602671206 2
 
< 0.1%
203102008 2
 
< 0.1%
501706002 2
 
< 0.1%
1300783000 2
 
< 0.1%
1603979000 2
 
< 0.1%
1702873000 1
 
< 0.1%
1702866000 1
 
< 0.1%
1702867000 1
 
< 0.1%
Other values (182225) 182225
> 99.9%
ValueCountFrequency (%)
100001000 1
< 0.1%
100002000 1
< 0.1%
100003000 1
< 0.1%
100004000 1
< 0.1%
100005000 1
< 0.1%
100006000 1
< 0.1%
100007000 1
< 0.1%
100008000 1
< 0.1%
100009000 1
< 0.1%
100010000 1
< 0.1%
ValueCountFrequency (%)
2205670000 1
< 0.1%
2205669000 1
< 0.1%
2205668000 1
< 0.1%
2205667000 1
< 0.1%
2205666000 1
< 0.1%
2205665004 1
< 0.1%
2205665002 1
< 0.1%
2205665000 1
< 0.1%
2205664000 1
< 0.1%
2205663001 1
< 0.1%

CM_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10770
Distinct (%)11.5%
Missing88951
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean9.1756087 × 108
Minimum1.00018 × 108
Maximum2.205665 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:50.261337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.00018 × 108
5-th percentile2.00204 × 108
Q13.06906 × 108
median6.026424 × 108
Q31.602331 × 109
95-th percentile2.102331 × 109
Maximum2.205665 × 109
Range2.105647 × 109
Interquartile range (IQR)1.295425 × 109

Descriptive statistics

Standard deviation6.8964632 × 108
Coefficient of variation (CV)0.75160825
Kurtosis-1.0506641
Mean9.1756087 × 108
Median Absolute Deviation (MAD)3.002954 × 108
Skewness0.71010661
Sum8.5600171 × 1013
Variance4.7561204 × 1017
MonotonicityNot monotonic
2024-09-12T14:38:50.347107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300450000 846
 
0.5%
300475000 816
 
0.4%
304850000 685
 
0.4%
306010010 538
 
0.3%
304590010 443
 
0.2%
306455010 425
 
0.2%
401149020 412
 
0.2%
203506010 370
 
0.2%
2205550001 355
 
0.2%
2101925000 339
 
0.2%
Other values (10760) 88062
48.3%
(Missing) 88951
48.8%
ValueCountFrequency (%)
100018000 5
< 0.1%
100019000 4
< 0.1%
100024000 4
< 0.1%
100041000 5
< 0.1%
100046000 3
< 0.1%
100109000 4
< 0.1%
100141000 4
< 0.1%
100145000 4
< 0.1%
100153000 5
< 0.1%
100154000 5
< 0.1%
ValueCountFrequency (%)
2205665000 3
 
< 0.1%
2205642000 3
 
< 0.1%
2205629000 3
 
< 0.1%
2205589000 3
 
< 0.1%
2205550001 355
0.2%
2205525000 5
 
< 0.1%
2205523000 94
 
0.1%
2205511000 3
 
< 0.1%
2205474000 9
 
< 0.1%
2205464000 4
 
< 0.1%

GIS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct98531
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1400938 × 109
Minimum1.00001 × 108
Maximum2.20567 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:50.431188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.00001 × 108
5-th percentile1.0619905 × 108
Q15.0158825 × 108
median1.1026175 × 109
Q31.810508 × 109
95-th percentile2.102473 × 109
Maximum2.20567 × 109
Range2.105669 × 109
Interquartile range (IQR)1.3089198 × 109

Descriptive statistics

Standard deviation7.0911226 × 108
Coefficient of variation (CV)0.62197712
Kurtosis-1.5581098
Mean1.1400938 × 109
Median Absolute Deviation (MAD)7.015645 × 108
Skewness0.03481601
Sum2.0777298 × 1014
Variance5.028402 × 1017
MonotonicityNot monotonic
2024-09-12T14:38:50.514510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300450000 865
 
0.5%
300475000 848
 
0.5%
304850000 701
 
0.4%
306010010 539
 
0.3%
306455010 462
 
0.3%
602642000 454
 
0.2%
304590010 444
 
0.2%
401149010 415
 
0.2%
203506010 370
 
0.2%
2205550001 355
 
0.2%
Other values (98521) 176789
97.0%
ValueCountFrequency (%)
100001000 1
< 0.1%
100002000 1
< 0.1%
100003000 1
< 0.1%
100004000 1
< 0.1%
100005000 1
< 0.1%
100006000 1
< 0.1%
100007000 1
< 0.1%
100008000 1
< 0.1%
100009000 1
< 0.1%
100010000 1
< 0.1%
ValueCountFrequency (%)
2205670000 1
 
< 0.1%
2205669000 1
 
< 0.1%
2205668000 1
 
< 0.1%
2205667000 1
 
< 0.1%
2205666000 1
 
< 0.1%
2205665000 3
< 0.1%
2205664000 1
 
< 0.1%
2205663001 1
 
< 0.1%
2205663000 1
 
< 0.1%
2205662020 1
 
< 0.1%

ST_NUM
Real number (ℝ)

MISSING 

Distinct2753
Distinct (%)1.6%
Missing9363
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean226.4317
Minimum0
Maximum5341
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:50.594891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median68
Q3212
95-th percentile945
Maximum5341
Range5341
Interquartile range (IQR)188

Descriptive statistics

Standard deviation475.70457
Coefficient of variation (CV)2.1008745
Kurtosis40.156888
Mean226.4317
Median Absolute Deviation (MAD)56
Skewness5.4182895
Sum39145285
Variance226294.83
MonotonicityNot monotonic
2024-09-12T14:38:50.679270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2890
 
1.6%
15 2685
 
1.5%
2 2633
 
1.4%
10 2456
 
1.3%
6 2339
 
1.3%
9 2315
 
1.3%
11 2129
 
1.2%
8 2061
 
1.1%
7 1842
 
1.0%
5 1836
 
1.0%
Other values (2743) 149693
82.1%
(Missing) 9363
 
5.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 2890
1.6%
2 2633
1.4%
3 1466
0.8%
4 1401
0.8%
5 1836
1.0%
6 2339
1.3%
7 1842
1.0%
8 2061
1.1%
9 2315
1.3%
ValueCountFrequency (%)
5341 1
 
< 0.1%
5337 5
< 0.1%
5335 1
 
< 0.1%
5330 1
 
< 0.1%
5321 1
 
< 0.1%
5318 1
 
< 0.1%
5314 1
 
< 0.1%
5313 1
 
< 0.1%
5309 1
 
< 0.1%
5305 1
 
< 0.1%
Distinct4510
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:50.918102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length10.551514
Min length4

Characters and Unicode

Total characters1922929
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)0.2%

Sample

1st rowPUTNAM ST
2nd rowLexington ST
3rd rowLexington ST
4th rowLexington ST
5th rowLexington ST
ValueCountFrequency (%)
st 123791
32.0%
av 25332
 
6.5%
rd 16142
 
4.2%
e 6110
 
1.6%
w 5141
 
1.3%
commonwealth 4994
 
1.3%
washington 4250
 
1.1%
pl 3482
 
0.9%
beacon 3470
 
0.9%
hill 3102
 
0.8%
Other values (3336) 191555
49.5%
2024-09-12T14:38:51.251212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
205139
 
10.7%
T 189045
 
9.8%
S 174821
 
9.1%
A 103657
 
5.4%
E 101553
 
5.3%
R 90042
 
4.7%
O 77275
 
4.0%
N 73625
 
3.8%
L 65593
 
3.4%
D 51927
 
2.7%
Other values (47) 790252
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1922929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
205139
 
10.7%
T 189045
 
9.8%
S 174821
 
9.1%
A 103657
 
5.4%
E 101553
 
5.3%
R 90042
 
4.7%
O 77275
 
4.0%
N 73625
 
3.8%
L 65593
 
3.4%
D 51927
 
2.7%
Other values (47) 790252
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1922929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
205139
 
10.7%
T 189045
 
9.8%
S 174821
 
9.1%
A 103657
 
5.4%
E 101553
 
5.3%
R 90042
 
4.7%
O 77275
 
4.0%
N 73625
 
3.8%
L 65593
 
3.4%
D 51927
 
2.7%
Other values (47) 790252
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1922929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
205139
 
10.7%
T 189045
 
9.8%
S 174821
 
9.1%
A 103657
 
5.4%
E 101553
 
5.3%
R 90042
 
4.7%
O 77275
 
4.0%
N 73625
 
3.8%
L 65593
 
3.4%
D 51927
 
2.7%
Other values (47) 790252
41.1%

UNIT_NUM
Text

MISSING 

Distinct14534
Distinct (%)17.6%
Missing99629
Missing (%)54.7%
Memory size1.4 MiB
2024-09-12T14:38:51.530393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length2.6008982
Min length1

Characters and Unicode

Total characters214868
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10328 ?
Unique (%)12.5%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row1
ValueCountFrequency (%)
1 8656
 
10.2%
2 8643
 
10.2%
3 6435
 
7.6%
4 2653
 
3.1%
5 1740
 
2.1%
6 1255
 
1.5%
ps 1164
 
1.4%
7 849
 
1.0%
8 747
 
0.9%
9 575
 
0.7%
Other values (13765) 52103
61.4%
2024-09-12T14:38:51.898591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36155
16.8%
2 27837
13.0%
3 20766
9.7%
- 19827
9.2%
0 18116
8.4%
4 14999
 
7.0%
5 11888
 
5.5%
6 9760
 
4.5%
7 7460
 
3.5%
8 6530
 
3.0%
Other values (47) 41530
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 36155
16.8%
2 27837
13.0%
3 20766
9.7%
- 19827
9.2%
0 18116
8.4%
4 14999
 
7.0%
5 11888
 
5.5%
6 9760
 
4.5%
7 7460
 
3.5%
8 6530
 
3.0%
Other values (47) 41530
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 36155
16.8%
2 27837
13.0%
3 20766
9.7%
- 19827
9.2%
0 18116
8.4%
4 14999
 
7.0%
5 11888
 
5.5%
6 9760
 
4.5%
7 7460
 
3.5%
8 6530
 
3.0%
Other values (47) 41530
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 36155
16.8%
2 27837
13.0%
3 20766
9.7%
- 19827
9.2%
0 18116
8.4%
4 14999
 
7.0%
5 11888
 
5.5%
6 9760
 
4.5%
7 7460
 
3.5%
8 6530
 
3.0%
Other values (47) 41530
19.3%

CITY
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size1.4 MiB
BOSTON
47713 
DORCHESTER
29328 
SOUTH BOSTON
15622 
JAMAICA PLAIN
12147 
BRIGHTON
12113 
Other values (14)
65316 

Length

Max length16
Median length12
Mean length9.206937
Min length6

Characters and Unicode

Total characters1677863
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEAST BOSTON
2nd rowEAST BOSTON
3rd rowEAST BOSTON
4th rowEAST BOSTON
5th rowEAST BOSTON

Common Values

ValueCountFrequency (%)
BOSTON 47713
26.2%
DORCHESTER 29328
16.1%
SOUTH BOSTON 15622
 
8.6%
JAMAICA PLAIN 12147
 
6.7%
BRIGHTON 12113
 
6.6%
WEST ROXBURY 11006
 
6.0%
EAST BOSTON 10233
 
5.6%
ROSLINDALE 9279
 
5.1%
HYDE PARK 9192
 
5.0%
CHARLESTOWN 7252
 
4.0%
Other values (9) 18354
 
10.1%

Length

2024-09-12T14:38:52.009869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boston 73568
30.2%
dorchester 29328
 
12.1%
roxbury 18991
 
7.8%
south 15622
 
6.4%
jamaica 12147
 
5.0%
plain 12147
 
5.0%
brighton 12113
 
5.0%
west 11006
 
4.5%
east 10233
 
4.2%
roslindale 9279
 
3.8%
Other values (12) 38868
16.0%

Most occurring characters

ValueCountFrequency (%)
O 246073
14.7%
T 175338
10.5%
S 165454
9.9%
R 136346
 
8.1%
N 126567
 
7.5%
E 106670
 
6.4%
B 104696
 
6.2%
A 103595
 
6.2%
H 75547
 
4.5%
61063
 
3.6%
Other values (14) 376514
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1677863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 246073
14.7%
T 175338
10.5%
S 165454
9.9%
R 136346
 
8.1%
N 126567
 
7.5%
E 106670
 
6.4%
B 104696
 
6.2%
A 103595
 
6.2%
H 75547
 
4.5%
61063
 
3.6%
Other values (14) 376514
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1677863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 246073
14.7%
T 175338
10.5%
S 165454
9.9%
R 136346
 
8.1%
N 126567
 
7.5%
E 106670
 
6.4%
B 104696
 
6.2%
A 103595
 
6.2%
H 75547
 
4.5%
61063
 
3.6%
Other values (14) 376514
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1677863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 246073
14.7%
T 175338
10.5%
S 165454
9.9%
R 136346
 
8.1%
N 126567
 
7.5%
E 106670
 
6.4%
B 104696
 
6.2%
A 103595
 
6.2%
H 75547
 
4.5%
61063
 
3.6%
Other values (14) 376514
22.4%

ZIP_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2129.8679
Minimum2026
Maximum2467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:52.079463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2026
5-th percentile2111
Q12119
median2127
Q32131
95-th percentile2136
Maximum2467
Range441
Interquartile range (IQR)12

Descriptive statistics

Standard deviation30.721915
Coefficient of variation (CV)0.014424328
Kurtosis81.218517
Mean2129.8679
Median Absolute Deviation (MAD)5
Skewness8.1238643
Sum3.88145 × 108
Variance943.83603
MonotonicityNot monotonic
2024-09-12T14:38:52.150717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2127 15656
 
8.6%
2130 12154
 
6.7%
2135 12114
 
6.6%
2124 11124
 
6.1%
2132 11004
 
6.0%
2128 10231
 
5.6%
2116 9649
 
5.3%
2118 9384
 
5.1%
2131 9283
 
5.1%
2136 9192
 
5.0%
Other values (27) 72448
39.8%
ValueCountFrequency (%)
2026 6
 
< 0.1%
2108 2172
 
1.2%
2109 1847
 
1.0%
2110 2487
 
1.4%
2111 2893
 
1.6%
2113 2357
 
1.3%
2114 5352
2.9%
2115 5548
3.0%
2116 9649
5.3%
2118 9384
5.1%
ValueCountFrequency (%)
2467 1017
 
0.6%
2458 1
 
< 0.1%
2446 11
 
< 0.1%
2445 13
 
< 0.1%
2219 1
 
< 0.1%
2215 3649
2.0%
2210 2132
1.2%
2201 3
 
< 0.1%
2199 36
 
< 0.1%
2137 2
 
< 0.1%

BLDG_SEQ
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
182242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182242
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 182242
100.0%

Length

2024-09-12T14:38:52.221645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:38:52.288159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 182242
100.0%

Most occurring characters

ValueCountFrequency (%)
1 182242
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 182242
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 182242
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 182242
100.0%

NUM_BLDGS
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
182228 
2
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182242
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

Length

2024-09-12T14:38:52.350595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:38:52.411400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 182228
> 99.9%
2 14
 
< 0.1%

LUC
Real number (ℝ)

HIGH CORRELATION 

Distinct201
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.58164
Minimum13
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:52.611305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile101
Q1102
median102
Q3108
95-th percentile995
Maximum995
Range982
Interquartile range (IQR)6

Descriptive statistics

Standard deviation266.13109
Coefficient of variation (CV)1.3136979
Kurtosis4.3641341
Mean202.58164
Median Absolute Deviation (MAD)1
Skewness2.4745422
Sum36918884
Variance70825.757
MonotonicityNot monotonic
2024-09-12T14:38:52.696990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 71974
39.5%
101 30439
16.7%
104 16814
 
9.2%
105 13298
 
7.3%
995 10768
 
5.9%
108 8451
 
4.6%
132 4185
 
2.3%
111 2496
 
1.4%
13 2270
 
1.2%
985 2185
 
1.2%
Other values (191) 19362
 
10.6%
ValueCountFrequency (%)
13 2270
 
1.2%
31 665
 
0.4%
101 30439
16.7%
102 71974
39.5%
103 2
 
< 0.1%
104 16814
 
9.2%
105 13298
 
7.3%
106 786
 
0.4%
108 8451
 
4.6%
109 170
 
0.1%
ValueCountFrequency (%)
995 10768
5.9%
992 10
 
< 0.1%
991 14
 
< 0.1%
990 5
 
< 0.1%
988 2
 
< 0.1%
987 59
 
< 0.1%
986 817
 
0.4%
985 2185
 
1.2%
983 16
 
< 0.1%
982 1
 
< 0.1%

LU
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
CD
71988 
R1
30441 
R2
16814 
R3
13468 
CM
10768 
Other values (12)
38763 

Length

Max length7
Median length2
Mean length2.0794548
Min length1

Characters and Unicode

Total characters378964
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR3
2nd rowR3
3rd rowR3
4th rowR3
5th rowR2

Common Values

ValueCountFrequency (%)
CD 71988
39.5%
R1 30441
16.7%
R2 16814
 
9.2%
R3 13468
 
7.4%
CM 10768
 
5.9%
CP 8451
 
4.6%
E 7610
 
4.2%
RL - RL 6030
 
3.3%
C 4658
 
2.6%
A 2964
 
1.6%
Other values (7) 9050
 
5.0%

Length

2024-09-12T14:38:52.779721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cd 71988
37.0%
r1 30441
15.7%
r2 16814
 
8.7%
r3 13468
 
6.9%
rl 12060
 
6.2%
cm 10768
 
5.5%
cp 8451
 
4.3%
e 7610
 
3.9%
6030
 
3.1%
c 4658
 
2.4%
Other values (8) 12014
 
6.2%

Most occurring characters

ValueCountFrequency (%)
C 103296
27.3%
R 78214
20.6%
D 71988
19.0%
1 30441
 
8.0%
2 16814
 
4.4%
3 13468
 
3.6%
L 13446
 
3.5%
12060
 
3.2%
M 10768
 
2.8%
P 8451
 
2.2%
Other values (6) 20018
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 103296
27.3%
R 78214
20.6%
D 71988
19.0%
1 30441
 
8.0%
2 16814
 
4.4%
3 13468
 
3.6%
L 13446
 
3.5%
12060
 
3.2%
M 10768
 
2.8%
P 8451
 
2.2%
Other values (6) 20018
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 103296
27.3%
R 78214
20.6%
D 71988
19.0%
1 30441
 
8.0%
2 16814
 
4.4%
3 13468
 
3.6%
L 13446
 
3.5%
12060
 
3.2%
M 10768
 
2.8%
P 8451
 
2.2%
Other values (6) 20018
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 103296
27.3%
R 78214
20.6%
D 71988
19.0%
1 30441
 
8.0%
2 16814
 
4.4%
3 13468
 
3.6%
L 13446
 
3.5%
12060
 
3.2%
M 10768
 
2.8%
P 8451
 
2.2%
Other values (6) 20018
 
5.3%
Distinct194
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:52.988050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length16.888231
Min length5

Characters and Unicode

Total characters3077745
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowTHREE-FAM DWELLING
2nd rowTHREE-FAM DWELLING
3rd rowTHREE-FAM DWELLING
4th rowTHREE-FAM DWELLING
5th rowTWO-FAM DWELLING
ValueCountFrequency (%)
condo 92825
21.9%
residential 72979
17.2%
dwelling 60551
14.3%
single 30439
 
7.2%
fam 30439
 
7.2%
two-fam 16814
 
4.0%
res 15805
 
3.7%
three-fam 13298
 
3.1%
main 10768
 
2.5%
parking 8987
 
2.1%
Other values (264) 71041
16.8%
2024-09-12T14:38:53.306844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 306547
 
10.0%
N 293643
 
9.5%
I 277658
 
9.0%
L 247639
 
8.0%
241900
 
7.9%
D 239004
 
7.8%
O 225960
 
7.3%
A 176683
 
5.7%
S 140752
 
4.6%
T 130272
 
4.2%
Other values (58) 797687
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3077745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 306547
 
10.0%
N 293643
 
9.5%
I 277658
 
9.0%
L 247639
 
8.0%
241900
 
7.9%
D 239004
 
7.8%
O 225960
 
7.3%
A 176683
 
5.7%
S 140752
 
4.6%
T 130272
 
4.2%
Other values (58) 797687
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3077745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 306547
 
10.0%
N 293643
 
9.5%
I 277658
 
9.0%
L 247639
 
8.0%
241900
 
7.9%
D 239004
 
7.8%
O 225960
 
7.3%
A 176683
 
5.7%
S 140752
 
4.6%
T 130272
 
4.2%
Other values (58) 797687
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3077745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 306547
 
10.0%
N 293643
 
9.5%
I 277658
 
9.0%
L 247639
 
8.0%
241900
 
7.9%
D 239004
 
7.8%
O 225960
 
7.3%
A 176683
 
5.7%
S 140752
 
4.6%
T 130272
 
4.2%
Other values (58) 797687
25.9%

BLDG_TYPE
Text

MISSING 

Distinct201
Distinct (%)0.1%
Missing2616
Missing (%)1.4%
Memory size1.4 MiB
2024-09-12T14:38:53.562281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length13.795586
Min length1

Characters and Unicode

Total characters2478046
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowRE - Row End
2nd rowRM - Row Middle
3rd rowRM - Row Middle
4th rowRM - Row Middle
5th rowRE - Row End
ValueCountFrequency (%)
168505
26.6%
rise 38625
 
6.1%
row 26473
 
4.2%
rm 17763
 
2.8%
middle 17698
 
2.8%
cl 16789
 
2.6%
colonial 16789
 
2.6%
lr 15448
 
2.4%
low 15448
 
2.4%
mr 15194
 
2.4%
Other values (462) 285650
45.0%
2024-09-12T14:38:53.904091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
454951
18.4%
- 179927
 
7.3%
R 147705
 
6.0%
e 145112
 
5.9%
i 129604
 
5.2%
o 126568
 
5.1%
n 106689
 
4.3%
d 85062
 
3.4%
a 82052
 
3.3%
l 79352
 
3.2%
Other values (59) 941024
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2478046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
454951
18.4%
- 179927
 
7.3%
R 147705
 
6.0%
e 145112
 
5.9%
i 129604
 
5.2%
o 126568
 
5.1%
n 106689
 
4.3%
d 85062
 
3.4%
a 82052
 
3.3%
l 79352
 
3.2%
Other values (59) 941024
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2478046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
454951
18.4%
- 179927
 
7.3%
R 147705
 
6.0%
e 145112
 
5.9%
i 129604
 
5.2%
o 126568
 
5.1%
n 106689
 
4.3%
d 85062
 
3.4%
a 82052
 
3.3%
l 79352
 
3.2%
Other values (59) 941024
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2478046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
454951
18.4%
- 179927
 
7.3%
R 147705
 
6.0%
e 145112
 
5.9%
i 129604
 
5.2%
o 126568
 
5.1%
n 106689
 
4.3%
d 85062
 
3.4%
a 82052
 
3.3%
l 79352
 
3.2%
Other values (59) 941024
38.0%

OWN_OCC
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.1 KiB
False
104952 
True
77290 
ValueCountFrequency (%)
False 104952
57.6%
True 77290
42.4%
2024-09-12T14:38:53.998530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OWNER
Text

Distinct143345
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:54.234039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length95
Median length73
Mean length18.269499
Min length3

Characters and Unicode

Total characters3329470
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124603 ?
Unique (%)68.4%

Sample

1st rowPASCUCCI CARLO
2nd rowSEMBRANO RODERICK
3rd rowGUERRA CHEVARRIA ANA S
4th rowJB REALTY TRUST
5th rowMARKS TRAVIS JOSEPH
ValueCountFrequency (%)
llc 22354
 
3.9%
trust 18834
 
3.3%
street 8131
 
1.4%
a 7749
 
1.4%
m 7240
 
1.3%
j 6867
 
1.2%
realty 6548
 
1.1%
of 5074
 
0.9%
boston 5042
 
0.9%
condo 4882
 
0.9%
Other values (66663) 478018
83.8%
2024-09-12T14:38:54.592325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
392298
11.8%
E 287947
 
8.6%
A 277585
 
8.3%
R 227193
 
6.8%
N 217593
 
6.5%
T 207937
 
6.2%
L 203316
 
6.1%
O 190013
 
5.7%
I 184146
 
5.5%
S 179232
 
5.4%
Other values (69) 962210
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3329470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
392298
11.8%
E 287947
 
8.6%
A 277585
 
8.3%
R 227193
 
6.8%
N 217593
 
6.5%
T 207937
 
6.2%
L 203316
 
6.1%
O 190013
 
5.7%
I 184146
 
5.5%
S 179232
 
5.4%
Other values (69) 962210
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3329470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
392298
11.8%
E 287947
 
8.6%
A 277585
 
8.3%
R 227193
 
6.8%
N 217593
 
6.5%
T 207937
 
6.2%
L 203316
 
6.1%
O 190013
 
5.7%
I 184146
 
5.5%
S 179232
 
5.4%
Other values (69) 962210
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3329470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
392298
11.8%
E 287947
 
8.6%
A 277585
 
8.3%
R 227193
 
6.8%
N 217593
 
6.5%
T 207937
 
6.2%
L 203316
 
6.1%
O 190013
 
5.7%
I 184146
 
5.5%
S 179232
 
5.4%
Other values (69) 962210
28.9%

MAIL_ADDRESSEE
Text

MISSING 

Distinct24033
Distinct (%)69.8%
Missing147830
Missing (%)81.1%
Memory size1.4 MiB
2024-09-12T14:38:54.825276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length84
Median length63
Mean length21.515082
Min length7

Characters and Unicode

Total characters740377
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20481 ?
Unique (%)59.5%

Sample

1st rowC/O LAUREN SCHOENADEL
2nd rowC/O ARTEM & CAITLYN SHKURATOV
3rd rowC/O MILDRED CASIELLO
4th rowC/O PETER LAPLANTE
5th rowC/O NASSER FARD
ValueCountFrequency (%)
c/o 34398
 
25.3%
llc 2892
 
2.1%
ts 2188
 
1.6%
1798
 
1.3%
inc 1314
 
1.0%
j 1197
 
0.9%
management 1068
 
0.8%
a 1027
 
0.8%
m 1011
 
0.7%
realty 948
 
0.7%
Other values (18312) 88013
64.8%
2024-09-12T14:38:55.164284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102450
13.8%
O 68138
 
9.2%
C 57372
 
7.7%
E 54226
 
7.3%
A 53772
 
7.3%
N 42498
 
5.7%
R 42209
 
5.7%
/ 35062
 
4.7%
I 34455
 
4.7%
L 34441
 
4.7%
Other values (50) 215754
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
102450
13.8%
O 68138
 
9.2%
C 57372
 
7.7%
E 54226
 
7.3%
A 53772
 
7.3%
N 42498
 
5.7%
R 42209
 
5.7%
/ 35062
 
4.7%
I 34455
 
4.7%
L 34441
 
4.7%
Other values (50) 215754
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
102450
13.8%
O 68138
 
9.2%
C 57372
 
7.7%
E 54226
 
7.3%
A 53772
 
7.3%
N 42498
 
5.7%
R 42209
 
5.7%
/ 35062
 
4.7%
I 34455
 
4.7%
L 34441
 
4.7%
Other values (50) 215754
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
102450
13.8%
O 68138
 
9.2%
C 57372
 
7.7%
E 54226
 
7.3%
A 53772
 
7.3%
N 42498
 
5.7%
R 42209
 
5.7%
/ 35062
 
4.7%
I 34455
 
4.7%
L 34441
 
4.7%
Other values (50) 215754
29.1%
Distinct142503
Distinct (%)78.2%
Missing4
Missing (%)< 0.1%
Memory size1.4 MiB
2024-09-12T14:38:55.449787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63
Median length48
Mean length16.857878
Min length1

Characters and Unicode

Total characters3072146
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125351 ?
Unique (%)68.8%

Sample

1st row195 LEXINGTON ST
2nd row197 LEXINGTON ST
3rd row199 LEXINGTON ST
4th rowPO BOX 557 #
5th row203 Lexington ST
ValueCountFrequency (%)
st 100012
 
14.7%
unit 29533
 
4.4%
rd 18882
 
2.8%
av 11821
 
1.7%
1 11615
 
1.7%
ave 11160
 
1.6%
2 10828
 
1.6%
3 7692
 
1.1%
e 4263
 
0.6%
4 4116
 
0.6%
Other values (20381) 468203
69.0%
2024-09-12T14:38:55.828424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
500810
 
16.3%
T 205716
 
6.7%
S 174198
 
5.7%
E 164084
 
5.3%
A 142235
 
4.6%
R 134766
 
4.4%
O 117647
 
3.8%
1 115240
 
3.8%
N 113509
 
3.7%
L 91393
 
3.0%
Other values (67) 1312548
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3072146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
500810
 
16.3%
T 205716
 
6.7%
S 174198
 
5.7%
E 164084
 
5.3%
A 142235
 
4.6%
R 134766
 
4.4%
O 117647
 
3.8%
1 115240
 
3.8%
N 113509
 
3.7%
L 91393
 
3.0%
Other values (67) 1312548
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3072146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
500810
 
16.3%
T 205716
 
6.7%
S 174198
 
5.7%
E 164084
 
5.3%
A 142235
 
4.6%
R 134766
 
4.4%
O 117647
 
3.8%
1 115240
 
3.8%
N 113509
 
3.7%
L 91393
 
3.0%
Other values (67) 1312548
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3072146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
500810
 
16.3%
T 205716
 
6.7%
S 174198
 
5.7%
E 164084
 
5.3%
A 142235
 
4.6%
R 134766
 
4.4%
O 117647
 
3.8%
1 115240
 
3.8%
N 113509
 
3.7%
L 91393
 
3.0%
Other values (67) 1312548
42.7%
Distinct2394
Distinct (%)1.3%
Missing11
Missing (%)< 0.1%
Memory size1.4 MiB
2024-09-12T14:38:56.039124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length30
Mean length8.9950283
Min length2

Characters and Unicode

Total characters1639173
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1162 ?
Unique (%)0.6%

Sample

1st rowEAST BOSTON
2nd rowEAST BOSTON
3rd rowEAST BOSTON
4th rowEVERETT
5th rowEAST BOSTON
ValueCountFrequency (%)
boston 61826
25.9%
dorchester 24084
 
10.1%
roxbury 15809
 
6.6%
south 11238
 
4.7%
jamaica 10649
 
4.5%
plain 10648
 
4.5%
west 10430
 
4.4%
roslindale 8304
 
3.5%
park 8152
 
3.4%
hyde 8070
 
3.4%
Other values (2112) 69163
29.0%
2024-09-12T14:38:56.330357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 224351
13.7%
T 158522
 
9.7%
S 145589
 
8.9%
N 129729
 
7.9%
R 128219
 
7.8%
E 118211
 
7.2%
A 105399
 
6.4%
B 92889
 
5.7%
H 69147
 
4.2%
L 58882
 
3.6%
Other values (59) 408235
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 224351
13.7%
T 158522
 
9.7%
S 145589
 
8.9%
N 129729
 
7.9%
R 128219
 
7.8%
E 118211
 
7.2%
A 105399
 
6.4%
B 92889
 
5.7%
H 69147
 
4.2%
L 58882
 
3.6%
Other values (59) 408235
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 224351
13.7%
T 158522
 
9.7%
S 145589
 
8.9%
N 129729
 
7.9%
R 128219
 
7.8%
E 118211
 
7.2%
A 105399
 
6.4%
B 92889
 
5.7%
H 69147
 
4.2%
L 58882
 
3.6%
Other values (59) 408235
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 224351
13.7%
T 158522
 
9.7%
S 145589
 
8.9%
N 129729
 
7.9%
R 128219
 
7.8%
E 118211
 
7.2%
A 105399
 
6.4%
B 92889
 
5.7%
H 69147
 
4.2%
L 58882
 
3.6%
Other values (59) 408235
24.9%
Distinct67
Distinct (%)< 0.1%
Missing319
Missing (%)0.2%
Memory size1.4 MiB
2024-09-12T14:38:56.461176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length2
Mean length2.0008355
Min length1

Characters and Unicode

Total characters363998
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowMA
2nd rowMA
3rd rowMA
4th rowMA
5th rowMA
ValueCountFrequency (%)
ma 173979
95.6%
ny 1250
 
0.7%
fl 1172
 
0.6%
ca 959
 
0.5%
nh 702
 
0.4%
tx 544
 
0.3%
ct 473
 
0.3%
nj 284
 
0.2%
ri 280
 
0.2%
me 203
 
0.1%
Other values (61) 2086
 
1.1%
2024-09-12T14:38:56.654004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 175610
48.2%
M 174515
47.9%
N 2568
 
0.7%
C 1824
 
0.5%
L 1338
 
0.4%
Y 1273
 
0.3%
F 1176
 
0.3%
T 1169
 
0.3%
H 852
 
0.2%
I 572
 
0.2%
Other values (30) 3101
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 363998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 175610
48.2%
M 174515
47.9%
N 2568
 
0.7%
C 1824
 
0.5%
L 1338
 
0.4%
Y 1273
 
0.3%
F 1176
 
0.3%
T 1169
 
0.3%
H 852
 
0.2%
I 572
 
0.2%
Other values (30) 3101
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 363998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 175610
48.2%
M 174515
47.9%
N 2568
 
0.7%
C 1824
 
0.5%
L 1338
 
0.4%
Y 1273
 
0.3%
F 1176
 
0.3%
T 1169
 
0.3%
H 852
 
0.2%
I 572
 
0.2%
Other values (30) 3101
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 363998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 175610
48.2%
M 174515
47.9%
N 2568
 
0.7%
C 1824
 
0.5%
L 1338
 
0.4%
Y 1273
 
0.3%
F 1176
 
0.3%
T 1169
 
0.3%
H 852
 
0.2%
I 572
 
0.2%
Other values (30) 3101
 
0.9%

MAIL_ZIP_CODE
Unsupported

REJECTED  UNSUPPORTED 

Missing61
Missing (%)< 0.1%
Memory size1.4 MiB

RES_FLOOR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)< 0.1%
Missing33792
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean1.8803537
Minimum0
Maximum62
Zeros31
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:56.747771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32.5
95-th percentile3
Maximum62
Range62
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1290417
Coefficient of variation (CV)0.60044113
Kurtosis321.52964
Mean1.8803537
Median Absolute Deviation (MAD)1
Skewness9.6638775
Sum279138.5
Variance1.2747351
MonotonicityNot monotonic
2024-09-12T14:38:56.830379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 63070
34.6%
2 42412
23.3%
3 24604
 
13.5%
2.5 7712
 
4.2%
4 4616
 
2.5%
1.5 3505
 
1.9%
5 1360
 
0.7%
3.5 487
 
0.3%
6 237
 
0.1%
4.5 105
 
0.1%
Other values (38) 342
 
0.2%
(Missing) 33792
18.5%
ValueCountFrequency (%)
0 31
 
< 0.1%
1 63070
34.6%
1.5 3505
 
1.9%
2 42412
23.3%
2.5 7712
 
4.2%
3 24604
 
13.5%
3.5 487
 
0.3%
4 4616
 
2.5%
4.5 105
 
0.1%
5 1360
 
0.7%
ValueCountFrequency (%)
62 1
 
< 0.1%
60 2
< 0.1%
46 3
< 0.1%
45 1
 
< 0.1%
41 2
< 0.1%
40 1
 
< 0.1%
39 2
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%

CD_FLOOR
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct60
Distinct (%)0.1%
Missing110270
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean3.5203135
Minimum0
Maximum60
Zeros8077
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:56.911760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile12
Maximum60
Range60
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2569276
Coefficient of variation (CV)1.4933124
Kurtosis28.761902
Mean3.5203135
Median Absolute Deviation (MAD)1
Skewness4.6866222
Sum253364
Variance27.635288
MonotonicityNot monotonic
2024-09-12T14:38:56.991914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 16764
 
9.2%
1 14700
 
8.1%
3 13735
 
7.5%
0 8077
 
4.4%
4 6543
 
3.6%
5 3448
 
1.9%
6 1762
 
1.0%
7 965
 
0.5%
8 707
 
0.4%
9 597
 
0.3%
Other values (50) 4674
 
2.6%
(Missing) 110270
60.5%
ValueCountFrequency (%)
0 8077
4.4%
1 14700
8.1%
2 16764
9.2%
3 13735
7.5%
4 6543
 
3.6%
5 3448
 
1.9%
6 1762
 
1.0%
7 965
 
0.5%
8 707
 
0.4%
9 597
 
0.3%
ValueCountFrequency (%)
60 4
 
< 0.1%
59 6
< 0.1%
58 7
< 0.1%
57 7
< 0.1%
56 9
< 0.1%
55 10
< 0.1%
54 10
< 0.1%
53 10
< 0.1%
52 10
< 0.1%
51 10
< 0.1%

RES_UNITS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct149
Distinct (%)1.4%
Missing171474
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean6.6886144
Minimum0
Maximum477
Zeros218
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:57.074206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q35
95-th percentile20
Maximum477
Range477
Interquartile range (IQR)3

Descriptive statistics

Standard deviation18.17673
Coefficient of variation (CV)2.7175628
Kurtosis209.23304
Mean6.6886144
Median Absolute Deviation (MAD)1
Skewness11.97815
Sum72023
Variance330.39351
MonotonicityNot monotonic
2024-09-12T14:38:57.239131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3962
 
2.2%
2 2784
 
1.5%
4 1057
 
0.6%
5 526
 
0.3%
6 519
 
0.3%
8 223
 
0.1%
0 218
 
0.1%
9 194
 
0.1%
7 149
 
0.1%
10 136
 
0.1%
Other values (139) 1000
 
0.5%
(Missing) 171474
94.1%
ValueCountFrequency (%)
0 218
 
0.1%
1 29
 
< 0.1%
2 2784
1.5%
3 3962
2.2%
4 1057
 
0.6%
5 526
 
0.3%
6 519
 
0.3%
7 149
 
0.1%
8 223
 
0.1%
9 194
 
0.1%
ValueCountFrequency (%)
477 1
< 0.1%
463 1
< 0.1%
442 1
< 0.1%
372 1
< 0.1%
367 1
< 0.1%
354 1
< 0.1%
338 1
< 0.1%
312 1
< 0.1%
311 1
< 0.1%
271 1
< 0.1%

COM_UNITS
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct24
Distinct (%)0.2%
Missing171474
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean0.17737741
Minimum0
Maximum212
Zeros10131
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:57.311033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum212
Range212
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6274729
Coefficient of variation (CV)14.812894
Kurtosis4459.5512
Mean0.17737741
Median Absolute Deviation (MAD)0
Skewness61.028972
Sum1910
Variance6.9036137
MonotonicityNot monotonic
2024-09-12T14:38:57.377673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 10131
 
5.6%
1 350
 
0.2%
2 134
 
0.1%
3 58
 
< 0.1%
4 24
 
< 0.1%
5 18
 
< 0.1%
6 10
 
< 0.1%
8 9
 
< 0.1%
7 8
 
< 0.1%
9 7
 
< 0.1%
Other values (14) 19
 
< 0.1%
(Missing) 171474
94.1%
ValueCountFrequency (%)
0 10131
5.6%
1 350
 
0.2%
2 134
 
0.1%
3 58
 
< 0.1%
4 24
 
< 0.1%
5 18
 
< 0.1%
6 10
 
< 0.1%
7 8
 
< 0.1%
8 9
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
212 1
< 0.1%
127 1
< 0.1%
60 1
< 0.1%
38 1
< 0.1%
26 2
< 0.1%
23 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
18 1
< 0.1%
15 1
< 0.1%

RC_UNITS
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct8
Distinct (%)0.1%
Missing171474
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean0.010958395
Minimum0
Maximum29
Zeros10731
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:57.435987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39999848
Coefficient of variation (CV)36.501556
Kurtosis3907.048
Mean0.010958395
Median Absolute Deviation (MAD)0
Skewness59.884204
Sum118
Variance0.15999878
MonotonicityNot monotonic
2024-09-12T14:38:57.496494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 10731
 
5.9%
1 24
 
< 0.1%
2 7
 
< 0.1%
5 2
 
< 0.1%
29 1
 
< 0.1%
3 1
 
< 0.1%
24 1
 
< 0.1%
14 1
 
< 0.1%
(Missing) 171474
94.1%
ValueCountFrequency (%)
0 10731
5.9%
1 24
 
< 0.1%
2 7
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
14 1
 
< 0.1%
24 1
 
< 0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
24 1
 
< 0.1%
14 1
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 7
 
< 0.1%
1 24
 
< 0.1%
0 10731
5.9%

LAND_SF
Text

MISSING 

Distinct17559
Distinct (%)10.1%
Missing8002
Missing (%)4.4%
Memory size1.4 MiB
2024-09-12T14:38:57.771127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length4.5646235
Min length3

Characters and Unicode

Total characters795340
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7616 ?
Unique (%)4.4%

Sample

1st row1,150
2nd row1,150
3rd row1,150
4th row1,150
5th row2,010
ValueCountFrequency (%)
5,000 2392
 
1.4%
4,000 1353
 
0.8%
2,500 862
 
0.5%
6,000 858
 
0.5%
4,500 671
 
0.4%
5,500 641
 
0.4%
3,600 536
 
0.3%
3,200 492
 
0.3%
3,000 490
 
0.3%
2,000 347
 
0.2%
Other values (17549) 165598
95.0%
2024-09-12T14:38:58.173432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 130489
16.4%
0 106022
13.3%
1 93910
11.8%
5 73860
9.3%
2 66439
8.4%
4 60552
7.6%
3 59000
7.4%
6 57057
7.2%
7 52775
6.6%
8 50775
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 795340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 130489
16.4%
0 106022
13.3%
1 93910
11.8%
5 73860
9.3%
2 66439
8.4%
4 60552
7.6%
3 59000
7.4%
6 57057
7.2%
7 52775
6.6%
8 50775
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 795340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 130489
16.4%
0 106022
13.3%
1 93910
11.8%
5 73860
9.3%
2 66439
8.4%
4 60552
7.6%
3 59000
7.4%
6 57057
7.2%
7 52775
6.6%
8 50775
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 795340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 130489
16.4%
0 106022
13.3%
1 93910
11.8%
5 73860
9.3%
2 66439
8.4%
4 60552
7.6%
3 59000
7.4%
6 57057
7.2%
7 52775
6.6%
8 50775
 
6.4%

GROSS_AREA
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct13098
Distinct (%)8.8%
Missing33848
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean5434.6768
Minimum3
Maximum6982322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:58.291706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile544
Q1967
median2085
Q34008
95-th percentile7770.7
Maximum6982322
Range6982319
Interquartile range (IQR)3041

Descriptive statistics

Standard deviation41322.818
Coefficient of variation (CV)7.6035465
Kurtosis6477.9546
Mean5434.6768
Median Absolute Deviation (MAD)1271
Skewness55.895966
Sum8.0647342 × 108
Variance1.7075753 × 109
MonotonicityNot monotonic
2024-09-12T14:38:58.379438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
780 282
 
0.2%
600 237
 
0.1%
700 230
 
0.1%
625 220
 
0.1%
690 214
 
0.1%
800 213
 
0.1%
1050 211
 
0.1%
760 210
 
0.1%
775 203
 
0.1%
730 197
 
0.1%
Other values (13088) 146177
80.2%
(Missing) 33848
 
18.6%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 1
 
< 0.1%
25 1
 
< 0.1%
42 1
 
< 0.1%
60 1
 
< 0.1%
82 1
 
< 0.1%
90 1
 
< 0.1%
100 125
0.1%
102 1
 
< 0.1%
106 1
 
< 0.1%
ValueCountFrequency (%)
6982322 1
< 0.1%
3064910 1
< 0.1%
2948448 1
< 0.1%
2481232 1
< 0.1%
2310322 1
< 0.1%
1976650 1
< 0.1%
1970176 1
< 0.1%
1933059 1
< 0.1%
1772572 1
< 0.1%
1726152 1
< 0.1%

LIVING_AREA
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct21808
Distinct (%)14.7%
Missing34141
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean4437.7612
Minimum2
Maximum6982322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:58.465801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile542
Q1942
median1483.5
Q32600
95-th percentile5768
Maximum6982322
Range6982320
Interquartile range (IQR)1658

Descriptive statistics

Standard deviation38453.214
Coefficient of variation (CV)8.665003
Kurtosis8373.4206
Mean4437.7612
Median Absolute Deviation (MAD)689.5
Skewness63.652416
Sum6.5723688 × 108
Variance1.4786497 × 109
MonotonicityNot monotonic
2024-09-12T14:38:58.554623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
780 291
 
0.2%
800 263
 
0.1%
1008 261
 
0.1%
1050 250
 
0.1%
1224 243
 
0.1%
600 239
 
0.1%
700 235
 
0.1%
625 223
 
0.1%
1000 222
 
0.1%
960 221
 
0.1%
Other values (21798) 145653
79.9%
(Missing) 34141
 
18.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
25 1
 
< 0.1%
42 1
 
< 0.1%
82 1
 
< 0.1%
90 1
 
< 0.1%
100 122
0.1%
102 1
 
< 0.1%
106 1
 
< 0.1%
108 1
 
< 0.1%
112 4
 
< 0.1%
ValueCountFrequency (%)
6982322 1
< 0.1%
2898078 1
< 0.1%
2882794 1
< 0.1%
2413114 1
< 0.1%
2310322 1
< 0.1%
1940476 1
< 0.1%
1885420 1
< 0.1%
1694084 1
< 0.1%
1595056 1
< 0.1%
1504200 1
< 0.1%
Distinct16658
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:58.769189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length3.909697
Min length1

Characters and Unicode

Total characters712511
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9985 ?
Unique (%)5.5%

Sample

1st row197,600
2nd row198,500
3rd row199,100
4th row199,700
5th row230,200
ValueCountFrequency (%)
0 94289
51.7%
218,300 66
 
< 0.1%
203,200 63
 
< 0.1%
238,200 59
 
< 0.1%
250,000 58
 
< 0.1%
233,500 57
 
< 0.1%
239,800 57
 
< 0.1%
240,900 57
 
< 0.1%
218,500 57
 
< 0.1%
229,900 57
 
< 0.1%
Other values (16648) 87422
48.0%
2024-09-12T14:38:59.078629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 288645
40.5%
, 94143
 
13.2%
2 63438
 
8.9%
1 51469
 
7.2%
3 39788
 
5.6%
4 32613
 
4.6%
5 30068
 
4.2%
6 28788
 
4.0%
7 28041
 
3.9%
8 27791
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 288645
40.5%
, 94143
 
13.2%
2 63438
 
8.9%
1 51469
 
7.2%
3 39788
 
5.6%
4 32613
 
4.6%
5 30068
 
4.2%
6 28788
 
4.0%
7 28041
 
3.9%
8 27791
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 288645
40.5%
, 94143
 
13.2%
2 63438
 
8.9%
1 51469
 
7.2%
3 39788
 
5.6%
4 32613
 
4.6%
5 30068
 
4.2%
6 28788
 
4.0%
7 28041
 
3.9%
8 27791
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 288645
40.5%
, 94143
 
13.2%
2 63438
 
8.9%
1 51469
 
7.2%
3 39788
 
5.6%
4 32613
 
4.6%
5 30068
 
4.2%
6 28788
 
4.0%
7 28041
 
3.9%
8 27791
 
3.9%
Distinct28352
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:38:59.325627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.5419278
Min length1

Characters and Unicode

Total characters1192214
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14035 ?
Unique (%)7.7%

Sample

1st row594,400
2nd row619,700
3rd row605,300
4th row535,600
5th row501,400
ValueCountFrequency (%)
0 20018
 
11.0%
200 2213
 
1.2%
60,000 674
 
0.4%
40,000 565
 
0.3%
43,000 481
 
0.3%
90,000 342
 
0.2%
38,000 308
 
0.2%
74,600 305
 
0.2%
48,000 280
 
0.2%
108,000 275
 
0.2%
Other values (28342) 156781
86.0%
2024-09-12T14:38:59.649213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 396479
33.3%
, 187305
15.7%
4 77028
 
6.5%
3 74480
 
6.2%
1 74375
 
6.2%
5 71824
 
6.0%
6 67827
 
5.7%
2 67112
 
5.6%
7 62524
 
5.2%
8 58227
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1192214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 396479
33.3%
, 187305
15.7%
4 77028
 
6.5%
3 74480
 
6.2%
1 74375
 
6.2%
5 71824
 
6.0%
6 67827
 
5.7%
2 67112
 
5.6%
7 62524
 
5.2%
8 58227
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1192214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 396479
33.3%
, 187305
15.7%
4 77028
 
6.5%
3 74480
 
6.2%
1 74375
 
6.2%
5 71824
 
6.0%
6 67827
 
5.7%
2 67112
 
5.6%
7 62524
 
5.2%
8 58227
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1192214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 396479
33.3%
, 187305
15.7%
4 77028
 
6.5%
3 74480
 
6.2%
1 74375
 
6.2%
5 71824
 
6.0%
6 67827
 
5.7%
2 67112
 
5.6%
7 62524
 
5.2%
8 58227
 
4.9%

SFYI_VALUE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
182242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182242
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 182242
100.0%

Length

2024-09-12T14:38:59.761868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:38:59.820357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 182242
100.0%

Most occurring characters

ValueCountFrequency (%)
0 182242
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 182242
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 182242
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 182242
100.0%
Distinct32201
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:00.037071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length7.0112323
Min length1

Characters and Unicode

Total characters1277741
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15263 ?
Unique (%)8.4%

Sample

1st row792,000
2nd row818,200
3rd row804,400
4th row735,300
5th row731,600
ValueCountFrequency (%)
0 10774
 
5.9%
60,000 681
 
0.4%
40,000 580
 
0.3%
43,000 491
 
0.3%
90,000 345
 
0.2%
38,000 318
 
0.2%
74,600 307
 
0.2%
48,000 293
 
0.2%
108,000 277
 
0.2%
47,000 272
 
0.1%
Other values (32191) 167904
92.1%
2024-09-12T14:39:00.381527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 413329
32.3%
, 212603
16.6%
1 90965
 
7.1%
4 76178
 
6.0%
5 74929
 
5.9%
6 73504
 
5.8%
3 71845
 
5.6%
7 69909
 
5.5%
2 67851
 
5.3%
8 64956
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1277741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 413329
32.3%
, 212603
16.6%
1 90965
 
7.1%
4 76178
 
6.0%
5 74929
 
5.9%
6 73504
 
5.8%
3 71845
 
5.6%
7 69909
 
5.5%
2 67851
 
5.3%
8 64956
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1277741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 413329
32.3%
, 212603
16.6%
1 90965
 
7.1%
4 76178
 
6.0%
5 74929
 
5.9%
6 73504
 
5.8%
3 71845
 
5.6%
7 69909
 
5.5%
2 67851
 
5.3%
8 64956
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1277741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 413329
32.3%
, 212603
16.6%
1 90965
 
7.1%
4 76178
 
6.0%
5 74929
 
5.9%
6 73504
 
5.8%
3 71845
 
5.6%
7 69909
 
5.5%
2 67851
 
5.3%
8 64956
 
5.1%
Distinct34946
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:00.614278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length10.613882
Min length6

Characters and Unicode

Total characters1934295
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18669 ?
Unique (%)10.2%

Sample

1st row $8,632.80
2nd row $8,918.38
3rd row $8,767.96
4th row $8,014.77
5th row $7,974.44
ValueCountFrequency (%)
18617
 
10.2%
654.00 676
 
0.4%
436.00 577
 
0.3%
468.70 441
 
0.2%
981.00 343
 
0.2%
813.14 305
 
0.2%
523.20 290
 
0.2%
414.20 286
 
0.2%
1,177.20 276
 
0.2%
512.30 268
 
0.1%
Other values (34936) 160163
87.9%
2024-09-12T14:39:00.915251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
401718
20.8%
$ 182242
9.4%
. 163625
8.5%
, 150824
 
7.8%
1 127167
 
6.6%
6 103329
 
5.3%
4 102418
 
5.3%
5 101394
 
5.2%
7 99792
 
5.2%
3 99129
 
5.1%
Other values (5) 402657
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1934295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
401718
20.8%
$ 182242
9.4%
. 163625
8.5%
, 150824
 
7.8%
1 127167
 
6.6%
6 103329
 
5.3%
4 102418
 
5.3%
5 101394
 
5.2%
7 99792
 
5.2%
3 99129
 
5.1%
Other values (5) 402657
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1934295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
401718
20.8%
$ 182242
9.4%
. 163625
8.5%
, 150824
 
7.8%
1 127167
 
6.6%
6 103329
 
5.3%
4 102418
 
5.3%
5 101394
 
5.2%
7 99792
 
5.2%
3 99129
 
5.1%
Other values (5) 402657
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1934295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
401718
20.8%
$ 182242
9.4%
. 163625
8.5%
, 150824
 
7.8%
1 127167
 
6.6%
6 103329
 
5.3%
4 102418
 
5.3%
5 101394
 
5.2%
7 99792
 
5.2%
3 99129
 
5.1%
Other values (5) 402657
20.8%

YR_BUILT
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct236
Distinct (%)0.1%
Missing22786
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean1933.2157
Minimum1700
Maximum20198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:01.030015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile1880
Q11900
median1920
Q31965
95-th percentile2017
Maximum20198
Range18498
Interquartile range (IQR)65

Descriptive statistics

Standard deviation63.981908
Coefficient of variation (CV)0.033096104
Kurtosis41646.839
Mean1933.2157
Median Absolute Deviation (MAD)21
Skewness146.10676
Sum3.0826284 × 108
Variance4093.6845
MonotonicityNot monotonic
2024-09-12T14:39:01.116224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1900 17988
 
9.9%
1920 12195
 
6.7%
1910 11790
 
6.5%
1905 10567
 
5.8%
1899 9866
 
5.4%
1890 8985
 
4.9%
1930 4176
 
2.3%
1999 3820
 
2.1%
1925 3785
 
2.1%
1880 3320
 
1.8%
Other values (226) 72964
40.0%
(Missing) 22786
 
12.5%
ValueCountFrequency (%)
1700 1
< 0.1%
1710 1
< 0.1%
1725 2
< 0.1%
1752 2
< 0.1%
1760 1
< 0.1%
1775 1
< 0.1%
1779 1
< 0.1%
1780 1
< 0.1%
1785 2
< 0.1%
1789 1
< 0.1%
ValueCountFrequency (%)
20198 1
 
< 0.1%
2023 8
 
< 0.1%
2022 340
 
0.2%
2021 1224
0.7%
2020 1613
0.9%
2019 1158
0.6%
2018 2437
1.3%
2017 2185
1.2%
2016 1603
0.9%
2015 1392
0.8%

YR_REMODEL
Real number (ℝ)

MISSING  SKEWED 

Distinct106
Distinct (%)0.1%
Missing95524
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean2002.0116
Minimum0
Maximum20220
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:01.202756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1979
Q11987
median2005
Q32016
95-th percentile2021
Maximum20220
Range20220
Interquartile range (IQR)29

Descriptive statistics

Standard deviation65.386476
Coefficient of variation (CV)0.032660387
Kurtosis69535.907
Mean2002.0116
Median Absolute Deviation (MAD)12
Skewness248.07278
Sum1.7361045 × 108
Variance4275.3912
MonotonicityNot monotonic
2024-09-12T14:39:01.291539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1985 3962
 
2.2%
2017 3910
 
2.1%
2021 3361
 
1.8%
2005 3207
 
1.8%
2019 3124
 
1.7%
1980 3112
 
1.7%
2018 3029
 
1.7%
2016 2893
 
1.6%
2022 2718
 
1.5%
2004 2711
 
1.5%
Other values (96) 54691
30.0%
(Missing) 95524
52.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
201 2
 
< 0.1%
221 1
 
< 0.1%
1900 9
< 0.1%
1902 1
 
< 0.1%
1904 1
 
< 0.1%
1910 1
 
< 0.1%
1914 3
 
< 0.1%
1915 1
 
< 0.1%
1916 2
 
< 0.1%
ValueCountFrequency (%)
20220 1
 
< 0.1%
2921 1
 
< 0.1%
2121 1
 
< 0.1%
2023 118
 
0.1%
2022 2718
1.5%
2021 3361
1.8%
2020 2624
1.4%
2019 3124
1.7%
2018 3029
1.7%
2017 3910
2.1%

STRUCTURE_CLASS
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing164836
Missing (%)90.4%
Memory size1.4 MiB
C - Brick/Concr
10201 
D - Wood/Frame
4528 
B - Reinf Concr
1649 
A - Struct Steel
 
925
E - Metal
 
103

Length

Max length16
Median length15
Mean length14.757497
Min length9

Characters and Unicode

Total characters256869
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD - Wood/Frame
2nd rowD - Wood/Frame
3rd rowD - Wood/Frame
4th rowD - Wood/Frame
5th rowD - Wood/Frame

Common Values

ValueCountFrequency (%)
C - Brick/Concr 10201
 
5.6%
D - Wood/Frame 4528
 
2.5%
B - Reinf Concr 1649
 
0.9%
A - Struct Steel 925
 
0.5%
E - Metal 103
 
0.1%
(Missing) 164836
90.4%

Length

2024-09-12T14:39:01.378149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:01.451454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
17406
31.8%
c 10201
18.6%
brick/concr 10201
18.6%
d 4528
 
8.3%
wood/frame 4528
 
8.3%
b 1649
 
3.0%
reinf 1649
 
3.0%
concr 1649
 
3.0%
a 925
 
1.7%
struct 925
 
1.7%
Other values (3) 1131
 
2.1%

Most occurring characters

ValueCountFrequency (%)
37386
14.6%
r 27504
10.7%
c 22976
8.9%
C 22051
 
8.6%
o 20906
 
8.1%
- 17406
 
6.8%
/ 14729
 
5.7%
n 13499
 
5.3%
B 11850
 
4.6%
i 11850
 
4.6%
Other values (17) 56712
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37386
14.6%
r 27504
10.7%
c 22976
8.9%
C 22051
 
8.6%
o 20906
 
8.1%
- 17406
 
6.8%
/ 14729
 
5.7%
n 13499
 
5.3%
B 11850
 
4.6%
i 11850
 
4.6%
Other values (17) 56712
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37386
14.6%
r 27504
10.7%
c 22976
8.9%
C 22051
 
8.6%
o 20906
 
8.1%
- 17406
 
6.8%
/ 14729
 
5.7%
n 13499
 
5.3%
B 11850
 
4.6%
i 11850
 
4.6%
Other values (17) 56712
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37386
14.6%
r 27504
10.7%
c 22976
8.9%
C 22051
 
8.6%
o 20906
 
8.1%
- 17406
 
6.8%
/ 14729
 
5.7%
n 13499
 
5.3%
B 11850
 
4.6%
i 11850
 
4.6%
Other values (17) 56712
22.1%

ROOF_STRUCTURE
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing36225
Missing (%)19.9%
Memory size1.4 MiB
F - Flat
68966 
G - Gable
46606 
H - Hip
14023 
M - Mansard
13574 
L - Gambrel
 
2153
Other values (2)
 
695

Length

Max length11
Median length9
Mean length8.5486279
Min length7

Characters and Unicode

Total characters1248245
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF - Flat
2nd rowF - Flat
3rd rowF - Flat
4th rowM - Mansard
5th rowM - Mansard

Common Values

ValueCountFrequency (%)
F - Flat 68966
37.8%
G - Gable 46606
25.6%
H - Hip 14023
 
7.7%
M - Mansard 13574
 
7.4%
L - Gambrel 2153
 
1.2%
S - Shed 350
 
0.2%
O - Other 345
 
0.2%
(Missing) 36225
19.9%

Length

2024-09-12T14:39:01.537900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:01.616428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
146017
33.3%
f 68966
15.7%
flat 68966
15.7%
g 46606
 
10.6%
gable 46606
 
10.6%
h 14023
 
3.2%
hip 14023
 
3.2%
m 13574
 
3.1%
mansard 13574
 
3.1%
l 2153
 
0.5%
Other values (5) 3543
 
0.8%

Most occurring characters

ValueCountFrequency (%)
292034
23.4%
- 146017
11.7%
a 144873
11.6%
F 137932
11.1%
l 117725
9.4%
G 95365
 
7.6%
t 69311
 
5.6%
e 49454
 
4.0%
b 48759
 
3.9%
H 28046
 
2.2%
Other values (12) 118729
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1248245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
292034
23.4%
- 146017
11.7%
a 144873
11.6%
F 137932
11.1%
l 117725
9.4%
G 95365
 
7.6%
t 69311
 
5.6%
e 49454
 
4.0%
b 48759
 
3.9%
H 28046
 
2.2%
Other values (12) 118729
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1248245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
292034
23.4%
- 146017
11.7%
a 144873
11.6%
F 137932
11.1%
l 117725
9.4%
G 95365
 
7.6%
t 69311
 
5.6%
e 49454
 
4.0%
b 48759
 
3.9%
H 28046
 
2.2%
Other values (12) 118729
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1248245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
292034
23.4%
- 146017
11.7%
a 144873
11.6%
F 137932
11.1%
l 117725
9.4%
G 95365
 
7.6%
t 69311
 
5.6%
e 49454
 
4.0%
b 48759
 
3.9%
H 28046
 
2.2%
Other values (12) 118729
9.5%

ROOF_COVER
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing36219
Missing (%)19.9%
Memory size1.4 MiB
A - Asphalt Shingl
63863 
R - Rubber Roof
46495 
C - Composition
22459 
S - Slate
11791 
O - Other
 
1032
Other values (2)
 
383

Length

Max length18
Median length16
Mean length15.773043
Min length8

Characters and Unicode

Total characters2303227
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC - Composition
2nd rowC - Composition
3rd rowC - Composition
4th rowC - Composition
5th rowC - Composition

Common Values

ValueCountFrequency (%)
A - Asphalt Shingl 63863
35.0%
R - Rubber Roof 46495
25.5%
C - Composition 22459
 
12.3%
S - Slate 11791
 
6.5%
O - Other 1032
 
0.6%
T - Tile 269
 
0.1%
W - Wood Shingle 114
 
0.1%
(Missing) 36219
19.9%

Length

2024-09-12T14:39:01.701175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:01.775502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
146023
26.6%
a 63863
11.6%
asphalt 63863
11.6%
shingl 63863
11.6%
r 46495
 
8.5%
rubber 46495
 
8.5%
roof 46495
 
8.5%
c 22459
 
4.1%
composition 22459
 
4.1%
slate 11791
 
2.1%
Other values (8) 14735
 
2.7%

Most occurring characters

ValueCountFrequency (%)
402518
17.5%
o 160595
 
7.0%
- 146023
 
6.3%
l 139900
 
6.1%
R 139485
 
6.1%
h 128872
 
5.6%
A 127726
 
5.5%
i 109164
 
4.7%
t 99145
 
4.3%
b 92990
 
4.0%
Other values (16) 756809
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2303227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
402518
17.5%
o 160595
 
7.0%
- 146023
 
6.3%
l 139900
 
6.1%
R 139485
 
6.1%
h 128872
 
5.6%
A 127726
 
5.5%
i 109164
 
4.7%
t 99145
 
4.3%
b 92990
 
4.0%
Other values (16) 756809
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2303227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
402518
17.5%
o 160595
 
7.0%
- 146023
 
6.3%
l 139900
 
6.1%
R 139485
 
6.1%
h 128872
 
5.6%
A 127726
 
5.5%
i 109164
 
4.7%
t 99145
 
4.3%
b 92990
 
4.0%
Other values (16) 756809
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2303227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
402518
17.5%
o 160595
 
7.0%
- 146023
 
6.3%
l 139900
 
6.1%
R 139485
 
6.1%
h 128872
 
5.6%
A 127726
 
5.5%
i 109164
 
4.7%
t 99145
 
4.3%
b 92990
 
4.0%
Other values (16) 756809
32.9%

INT_WALL
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing48749
Missing (%)26.7%
Memory size1.4 MiB
N - Normal
128622 
E - Elaborate
 
4788
S - Substandard
 
80
G - Good
 
3

Length

Max length15
Median length10
Mean length10.110553
Min length8

Characters and Unicode

Total characters1349688
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN - Normal
2nd rowN - Normal
3rd rowN - Normal
4th rowN - Normal
5th rowN - Normal

Common Values

ValueCountFrequency (%)
N - Normal 128622
70.6%
E - Elaborate 4788
 
2.6%
S - Substandard 80
 
< 0.1%
G - Good 3
 
< 0.1%
(Missing) 48749
 
26.7%

Length

2024-09-12T14:39:01.864638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:02.016412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
133493
33.3%
n 128622
32.1%
normal 128622
32.1%
e 4788
 
1.2%
elaborate 4788
 
1.2%
s 80
 
< 0.1%
substandard 80
 
< 0.1%
g 3
 
< 0.1%
good 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
266986
19.8%
N 257244
19.1%
a 138358
10.3%
- 133493
9.9%
r 133490
9.9%
o 133416
9.9%
l 133410
9.9%
m 128622
9.5%
E 9576
 
0.7%
t 4868
 
0.4%
Other values (8) 10225
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1349688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
266986
19.8%
N 257244
19.1%
a 138358
10.3%
- 133493
9.9%
r 133490
9.9%
o 133416
9.9%
l 133410
9.9%
m 128622
9.5%
E 9576
 
0.7%
t 4868
 
0.4%
Other values (8) 10225
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1349688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
266986
19.8%
N 257244
19.1%
a 138358
10.3%
- 133493
9.9%
r 133490
9.9%
o 133416
9.9%
l 133410
9.9%
m 128622
9.5%
E 9576
 
0.7%
t 4868
 
0.4%
Other values (8) 10225
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1349688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
266986
19.8%
N 257244
19.1%
a 138358
10.3%
- 133493
9.9%
r 133490
9.9%
o 133416
9.9%
l 133410
9.9%
m 128622
9.5%
E 9576
 
0.7%
t 4868
 
0.4%
Other values (8) 10225
 
0.8%

EXT_FNISHED
Categorical

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)< 0.1%
Missing22884
Missing (%)12.6%
Memory size1.4 MiB
B - Brick/Stone
52403 
M - Vinyl
42605 
W - Wood Shake
16453 
F - Frame/Clapbrd
13337 
C - Cement Board
8992 
Other values (23)
25568 

Length

Max length18
Median length17
Mean length13.014464
Min length9

Characters and Unicode

Total characters2073959
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA - Asbestos
2nd rowM - Vinyl
3rd rowM - Vinyl
4th rowM - Vinyl
5th rowM - Vinyl

Common Values

ValueCountFrequency (%)
B - Brick/Stone 52403
28.8%
M - Vinyl 42605
23.4%
W - Wood Shake 16453
 
9.0%
F - Frame/Clapbrd 13337
 
7.3%
C - Cement Board 8992
 
4.9%
01 - Brick 7693
 
4.2%
A - Asbestos 3967
 
2.2%
G - Glass 3586
 
2.0%
09 - Wood Siding 1762
 
1.0%
S - Stucco 1309
 
0.7%
Other values (18) 7251
 
4.0%
(Missing) 22884
12.6%

Length

2024-09-12T14:39:02.085192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
159427
31.3%
brick/stone 52403
 
10.3%
b 52403
 
10.3%
m 42605
 
8.4%
vinyl 42605
 
8.4%
wood 18215
 
3.6%
w 16453
 
3.2%
shake 16453
 
3.2%
f 13337
 
2.6%
frame/clapbrd 13337
 
2.6%
Other values (52) 82230
16.1%

Most occurring characters

ValueCountFrequency (%)
350110
16.9%
- 159358
 
7.7%
B 123354
 
5.9%
n 112511
 
5.4%
i 109081
 
5.3%
e 108101
 
5.2%
o 107712
 
5.2%
r 101306
 
4.9%
k 78411
 
3.8%
S 75848
 
3.7%
Other values (39) 748167
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2073959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
350110
16.9%
- 159358
 
7.7%
B 123354
 
5.9%
n 112511
 
5.4%
i 109081
 
5.3%
e 108101
 
5.2%
o 107712
 
5.2%
r 101306
 
4.9%
k 78411
 
3.8%
S 75848
 
3.7%
Other values (39) 748167
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2073959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
350110
16.9%
- 159358
 
7.7%
B 123354
 
5.9%
n 112511
 
5.4%
i 109081
 
5.3%
e 108101
 
5.2%
o 107712
 
5.2%
r 101306
 
4.9%
k 78411
 
3.8%
S 75848
 
3.7%
Other values (39) 748167
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2073959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
350110
16.9%
- 159358
 
7.7%
B 123354
 
5.9%
n 112511
 
5.4%
i 109081
 
5.3%
e 108101
 
5.2%
o 107712
 
5.2%
r 101306
 
4.9%
k 78411
 
3.8%
S 75848
 
3.7%
Other values (39) 748167
36.1%

INT_COND
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing48746
Missing (%)26.7%
Memory size1.4 MiB
A - Average
63032 
G - Good
54911 
E - Excellent
14267 
F - Fair
 
1198
P - Poor
 
88

Length

Max length13
Median length11
Mean length9.9508525
Min length8

Characters and Unicode

Total characters1328399
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA - Average
2nd rowA - Average
3rd rowA - Average
4th rowA - Average
5th rowA - Average

Common Values

ValueCountFrequency (%)
A - Average 63032
34.6%
G - Good 54911
30.1%
E - Excellent 14267
 
7.8%
F - Fair 1198
 
0.7%
P - Poor 88
 
< 0.1%
(Missing) 48746
26.7%

Length

2024-09-12T14:39:02.154635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:02.219425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
133496
33.3%
a 63032
15.7%
average 63032
15.7%
g 54911
13.7%
good 54911
13.7%
e 14267
 
3.6%
excellent 14267
 
3.6%
f 1198
 
0.3%
fair 1198
 
0.3%
p 88
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
266992
20.1%
e 154598
11.6%
- 133496
10.0%
A 126064
9.5%
o 109998
8.3%
G 109822
8.3%
r 64318
 
4.8%
a 64230
 
4.8%
v 63032
 
4.7%
g 63032
 
4.7%
Other values (10) 172817
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1328399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
266992
20.1%
e 154598
11.6%
- 133496
10.0%
A 126064
9.5%
o 109998
8.3%
G 109822
8.3%
r 64318
 
4.8%
a 64230
 
4.8%
v 63032
 
4.7%
g 63032
 
4.7%
Other values (10) 172817
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1328399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
266992
20.1%
e 154598
11.6%
- 133496
10.0%
A 126064
9.5%
o 109998
8.3%
G 109822
8.3%
r 64318
 
4.8%
a 64230
 
4.8%
v 63032
 
4.7%
g 63032
 
4.7%
Other values (10) 172817
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1328399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
266992
20.1%
e 154598
11.6%
- 133496
10.0%
A 126064
9.5%
o 109998
8.3%
G 109822
8.3%
r 64318
 
4.8%
a 64230
 
4.8%
v 63032
 
4.7%
g 63032
 
4.7%
Other values (10) 172817
13.0%

EXT_COND
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing36158
Missing (%)19.8%
Memory size1.4 MiB
A - Average
78882 
G - Good
55519 
E - Excellent
9407 
F - Fair
 
2215
P - Poor
 
61

Length

Max length13
Median length11
Mean length9.9419033
Min length8

Characters and Unicode

Total characters1452353
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF - Fair
2nd rowA - Average
3rd rowG - Good
4th rowA - Average
5th rowF - Fair

Common Values

ValueCountFrequency (%)
A - Average 78882
43.3%
G - Good 55519
30.5%
E - Excellent 9407
 
5.2%
F - Fair 2215
 
1.2%
P - Poor 61
 
< 0.1%
(Missing) 36158
19.8%

Length

2024-09-12T14:39:02.293058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:02.356476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
146084
33.3%
a 78882
18.0%
average 78882
18.0%
g 55519
 
12.7%
good 55519
 
12.7%
e 9407
 
2.1%
excellent 9407
 
2.1%
f 2215
 
0.5%
fair 2215
 
0.5%
p 61
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
292168
20.1%
e 176578
12.2%
A 157764
10.9%
- 146084
10.1%
o 111160
 
7.7%
G 111038
 
7.6%
r 81158
 
5.6%
a 81097
 
5.6%
v 78882
 
5.4%
g 78882
 
5.4%
Other values (10) 137542
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1452353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
292168
20.1%
e 176578
12.2%
A 157764
10.9%
- 146084
10.1%
o 111160
 
7.7%
G 111038
 
7.6%
r 81158
 
5.6%
a 81097
 
5.6%
v 78882
 
5.4%
g 78882
 
5.4%
Other values (10) 137542
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1452353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
292168
20.1%
e 176578
12.2%
A 157764
10.9%
- 146084
10.1%
o 111160
 
7.7%
G 111038
 
7.6%
r 81158
 
5.6%
a 81097
 
5.6%
v 78882
 
5.4%
g 78882
 
5.4%
Other values (10) 137542
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1452353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
292168
20.1%
e 176578
12.2%
A 157764
10.9%
- 146084
10.1%
o 111160
 
7.7%
G 111038
 
7.6%
r 81158
 
5.6%
a 81097
 
5.6%
v 78882
 
5.4%
g 78882
 
5.4%
Other values (10) 137542
9.5%

OVERALL_COND
Categorical

IMBALANCE  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing9587
Missing (%)5.3%
Memory size1.4 MiB
A - Average
130657 
G - Good
36490 
E - Excellent
 
1728
VG - Very Good
 
1380
EX - Excellent
 
1252
Other values (5)
 
1148

Length

Max length23
Median length11
Mean length10.412684
Min length8

Characters and Unicode

Total characters1797802
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA - Average
2nd rowA - Average
3rd rowA - Average
4th rowA - Average
5th rowA - Average

Common Values

ValueCountFrequency (%)
A - Average 130657
71.7%
G - Good 36490
 
20.0%
E - Excellent 1728
 
0.9%
VG - Very Good 1380
 
0.8%
EX - Excellent 1252
 
0.7%
F - Fair 1019
 
0.6%
P - Poor 98
 
0.1%
US - Unsound 18
 
< 0.1%
VP - Very Poor 12
 
< 0.1%
AVG - Default - Average 1
 
< 0.1%
(Missing) 9587
 
5.3%

Length

2024-09-12T14:39:02.436742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:02.516532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
172656
33.2%
average 130658
25.2%
a 130657
25.2%
good 37870
 
7.3%
g 36490
 
7.0%
excellent 2980
 
0.6%
e 1728
 
0.3%
very 1392
 
0.3%
vg 1380
 
0.3%
ex 1252
 
0.2%
Other values (9) 2296
 
0.4%

Most occurring characters

ValueCountFrequency (%)
346704
19.3%
e 268669
14.9%
A 261316
14.5%
- 172656
9.6%
r 133179
 
7.4%
a 131678
 
7.3%
v 130658
 
7.3%
g 130658
 
7.3%
o 75978
 
4.2%
G 75741
 
4.2%
Other values (19) 70565
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1797802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
346704
19.3%
e 268669
14.9%
A 261316
14.5%
- 172656
9.6%
r 133179
 
7.4%
a 131678
 
7.3%
v 130658
 
7.3%
g 130658
 
7.3%
o 75978
 
4.2%
G 75741
 
4.2%
Other values (19) 70565
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1797802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
346704
19.3%
e 268669
14.9%
A 261316
14.5%
- 172656
9.6%
r 133179
 
7.4%
a 131678
 
7.3%
v 130658
 
7.3%
g 130658
 
7.3%
o 75978
 
4.2%
G 75741
 
4.2%
Other values (19) 70565
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1797802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
346704
19.3%
e 268669
14.9%
A 261316
14.5%
- 172656
9.6%
r 133179
 
7.4%
a 131678
 
7.3%
v 130658
 
7.3%
g 130658
 
7.3%
o 75978
 
4.2%
G 75741
 
4.2%
Other values (19) 70565
 
3.9%

BED_RMS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing48765
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean3.1484376
Minimum0
Maximum21
Zeros3184
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:02.643202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile8
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1022194
Coefficient of variation (CV)0.66770242
Kurtosis2.2563752
Mean3.1484376
Median Absolute Deviation (MAD)1
Skewness1.3651924
Sum420244
Variance4.4193264
MonotonicityNot monotonic
2024-09-12T14:39:02.711657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 38002
20.9%
3 27552
15.1%
1 22279
12.2%
4 15328
 
8.4%
6 9957
 
5.5%
5 8124
 
4.5%
9 3275
 
1.8%
0 3184
 
1.7%
8 2297
 
1.3%
7 2208
 
1.2%
Other values (9) 1271
 
0.7%
(Missing) 48765
26.8%
ValueCountFrequency (%)
0 3184
 
1.7%
1 22279
12.2%
2 38002
20.9%
3 27552
15.1%
4 15328
8.4%
5 8124
 
4.5%
6 9957
 
5.5%
7 2208
 
1.2%
8 2297
 
1.3%
9 3275
 
1.8%
ValueCountFrequency (%)
21 1
 
< 0.1%
17 5
 
< 0.1%
16 2
 
< 0.1%
15 27
 
< 0.1%
14 54
 
< 0.1%
13 32
 
< 0.1%
12 351
 
0.2%
11 407
 
0.2%
10 392
 
0.2%
9 3275
1.8%

FULL_BTH
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing11644
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1.359758
Minimum0
Maximum21
Zeros36940
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:02.777886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0605678
Coefficient of variation (CV)0.77996806
Kurtosis3.1724521
Mean1.359758
Median Absolute Deviation (MAD)1
Skewness0.921087
Sum231972
Variance1.1248041
MonotonicityNot monotonic
2024-09-12T14:39:02.840617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 64877
35.6%
2 45213
24.8%
0 36940
20.3%
3 19794
 
10.9%
4 2551
 
1.4%
6 580
 
0.3%
5 519
 
0.3%
7 65
 
< 0.1%
8 35
 
< 0.1%
9 12
 
< 0.1%
Other values (7) 12
 
< 0.1%
(Missing) 11644
 
6.4%
ValueCountFrequency (%)
0 36940
20.3%
1 64877
35.6%
2 45213
24.8%
3 19794
 
10.9%
4 2551
 
1.4%
5 519
 
0.3%
6 580
 
0.3%
7 65
 
< 0.1%
8 35
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 3
 
< 0.1%
12 2
 
< 0.1%
10 2
 
< 0.1%
9 12
 
< 0.1%
8 35
< 0.1%
7 65
< 0.1%

HLF_BTH
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing11509
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean0.22192546
Minimum0
Maximum7
Zeros135661
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:02.899163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46002771
Coefficient of variation (CV)2.0728929
Kurtosis5.6723827
Mean0.22192546
Median Absolute Deviation (MAD)0
Skewness2.1362235
Sum37890
Variance0.2116255
MonotonicityNot monotonic
2024-09-12T14:39:02.962754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 135661
74.4%
1 32695
 
17.9%
2 1983
 
1.1%
3 361
 
0.2%
4 23
 
< 0.1%
5 7
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 11509
 
6.3%
ValueCountFrequency (%)
0 135661
74.4%
1 32695
 
17.9%
2 1983
 
1.1%
3 361
 
0.2%
4 23
 
< 0.1%
5 7
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 7
 
< 0.1%
4 23
 
< 0.1%
3 361
 
0.2%
2 1983
 
1.1%
1 32695
 
17.9%
0 135661
74.4%

KITCHENS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing11718
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1.0518813
Minimum0
Maximum5
Zeros36863
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:03.026203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8053745
Coefficient of variation (CV)0.76565153
Kurtosis0.7498689
Mean1.0518813
Median Absolute Deviation (MAD)0
Skewness0.8745803
Sum179371
Variance0.64862808
MonotonicityNot monotonic
2024-09-12T14:39:03.088105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 102061
56.0%
0 36863
 
20.2%
2 17625
 
9.7%
3 13841
 
7.6%
4 133
 
0.1%
5 1
 
< 0.1%
(Missing) 11718
 
6.4%
ValueCountFrequency (%)
0 36863
 
20.2%
1 102061
56.0%
2 17625
 
9.7%
3 13841
 
7.6%
4 133
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 133
 
0.1%
3 13841
 
7.6%
2 17625
 
9.7%
1 102061
56.0%
0 36863
 
20.2%

TT_RMS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)< 0.1%
Missing48829
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean6.940583
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:03.149426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile15
Maximum20
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0097978
Coefficient of variation (CV)0.57773214
Kurtosis0.54651164
Mean6.940583
Median Absolute Deviation (MAD)2
Skewness1.1306302
Sum925964
Variance16.078479
MonotonicityNot monotonic
2024-09-12T14:39:03.220164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 24165
13.3%
5 18593
 
10.2%
3 15940
 
8.7%
6 15592
 
8.6%
7 10547
 
5.8%
8 7496
 
4.1%
10 5715
 
3.1%
12 5159
 
2.8%
9 4870
 
2.7%
2 4729
 
2.6%
Other values (10) 20607
11.3%
(Missing) 48829
26.8%
ValueCountFrequency (%)
1 697
 
0.4%
2 4729
 
2.6%
3 15940
8.7%
4 24165
13.3%
5 18593
10.2%
6 15592
8.6%
7 10547
5.8%
8 7496
 
4.1%
9 4870
 
2.7%
10 5715
 
3.1%
ValueCountFrequency (%)
20 598
 
0.3%
19 183
 
0.1%
18 2387
1.3%
17 1286
 
0.7%
16 993
 
0.5%
15 4658
2.6%
14 3195
1.8%
13 2332
1.3%
12 5159
2.8%
11 4278
2.3%

BDRM_COND
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing110500
Missing (%)60.6%
Memory size1.4 MiB
A - Average
56687 
G - Good
13194 
E - Excellent
 
962
F - Fair
 
837
P - Poor
 
62

Length

Max length13
Median length11
Mean length10.437498
Min length8

Characters and Unicode

Total characters748807
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA - Average
2nd rowA - Average
3rd rowA - Average
4th rowA - Average
5th rowA - Average

Common Values

ValueCountFrequency (%)
A - Average 56687
31.1%
G - Good 13194
 
7.2%
E - Excellent 962
 
0.5%
F - Fair 837
 
0.5%
P - Poor 62
 
< 0.1%
(Missing) 110500
60.6%

Length

2024-09-12T14:39:03.295656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:03.362070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
71742
33.3%
a 56687
26.3%
average 56687
26.3%
g 13194
 
6.1%
good 13194
 
6.1%
e 962
 
0.4%
excellent 962
 
0.4%
f 837
 
0.4%
fair 837
 
0.4%
p 62
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
143484
19.2%
e 115298
15.4%
A 113374
15.1%
- 71742
9.6%
r 57586
7.7%
a 57524
7.7%
v 56687
 
7.6%
g 56687
 
7.6%
o 26512
 
3.5%
G 26388
 
3.5%
Other values (10) 23525
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 748807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
143484
19.2%
e 115298
15.4%
A 113374
15.1%
- 71742
9.6%
r 57586
7.7%
a 57524
7.7%
v 56687
 
7.6%
g 56687
 
7.6%
o 26512
 
3.5%
G 26388
 
3.5%
Other values (10) 23525
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 748807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
143484
19.2%
e 115298
15.4%
A 113374
15.1%
- 71742
9.6%
r 57586
7.7%
a 57524
7.7%
v 56687
 
7.6%
g 56687
 
7.6%
o 26512
 
3.5%
G 26388
 
3.5%
Other values (10) 23525
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 748807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
143484
19.2%
e 115298
15.4%
A 113374
15.1%
- 71742
9.6%
r 57586
7.7%
a 57524
7.7%
v 56687
 
7.6%
g 56687
 
7.6%
o 26512
 
3.5%
G 26388
 
3.5%
Other values (10) 23525
 
3.1%

BTHRM_STYLE1
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing49548
Missing (%)27.2%
Memory size1.4 MiB
M - Modern
61020 
S - Semi-Modern
58554 
L - Luxury
7458 
N - No Remodeling
 
5662

Length

Max length17
Median length10
Mean length12.505042
Min length10

Characters and Unicode

Total characters1659344
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowM - Modern
4th rowS - Semi-Modern
5th rowN - No Remodeling

Common Values

ValueCountFrequency (%)
M - Modern 61020
33.5%
S - Semi-Modern 58554
32.1%
L - Luxury 7458
 
4.1%
N - No Remodeling 5662
 
3.1%
(Missing) 49548
27.2%

Length

2024-09-12T14:39:03.441793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:03.509687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
132694
32.9%
m 61020
15.1%
modern 61020
15.1%
s 58554
14.5%
semi-modern 58554
14.5%
l 7458
 
1.8%
luxury 7458
 
1.8%
n 5662
 
1.4%
no 5662
 
1.4%
remodeling 5662
 
1.4%

Most occurring characters

ValueCountFrequency (%)
271050
16.3%
- 191248
11.5%
e 189452
11.4%
M 180594
10.9%
o 130898
7.9%
r 127032
7.7%
d 125236
7.5%
n 125236
7.5%
S 117108
7.1%
i 64216
 
3.9%
Other values (9) 137274
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1659344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
271050
16.3%
- 191248
11.5%
e 189452
11.4%
M 180594
10.9%
o 130898
7.9%
r 127032
7.7%
d 125236
7.5%
n 125236
7.5%
S 117108
7.1%
i 64216
 
3.9%
Other values (9) 137274
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1659344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
271050
16.3%
- 191248
11.5%
e 189452
11.4%
M 180594
10.9%
o 130898
7.9%
r 127032
7.7%
d 125236
7.5%
n 125236
7.5%
S 117108
7.1%
i 64216
 
3.9%
Other values (9) 137274
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1659344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
271050
16.3%
- 191248
11.5%
e 189452
11.4%
M 180594
10.9%
o 130898
7.9%
r 127032
7.7%
d 125236
7.5%
n 125236
7.5%
S 117108
7.1%
i 64216
 
3.9%
Other values (9) 137274
8.3%

BTHRM_STYLE2
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing97077
Missing (%)53.3%
Memory size1.4 MiB
M - Modern
41164 
S - Semi-Modern
36236 
N - No Remodeling
 
3920
L - Luxury
 
3845

Length

Max length17
Median length10
Mean length12.449598
Min length10

Characters and Unicode

Total characters1060270
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowM - Modern
4th rowS - Semi-Modern
5th rowN - No Remodeling

Common Values

ValueCountFrequency (%)
M - Modern 41164
22.6%
S - Semi-Modern 36236
 
19.9%
N - No Remodeling 3920
 
2.2%
L - Luxury 3845
 
2.1%
(Missing) 97077
53.3%

Length

2024-09-12T14:39:03.583486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:03.653275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
85165
32.8%
m 41164
15.9%
modern 41164
15.9%
s 36236
14.0%
semi-modern 36236
14.0%
n 3920
 
1.5%
no 3920
 
1.5%
remodeling 3920
 
1.5%
l 3845
 
1.5%
luxury 3845
 
1.5%

Most occurring characters

ValueCountFrequency (%)
174250
16.4%
e 121476
11.5%
- 121401
11.5%
M 118564
11.2%
o 85240
8.0%
d 81320
7.7%
n 81320
7.7%
r 81245
7.7%
S 72472
6.8%
i 40156
 
3.8%
Other values (9) 82826
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1060270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
174250
16.4%
e 121476
11.5%
- 121401
11.5%
M 118564
11.2%
o 85240
8.0%
d 81320
7.7%
n 81320
7.7%
r 81245
7.7%
S 72472
6.8%
i 40156
 
3.8%
Other values (9) 82826
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1060270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
174250
16.4%
e 121476
11.5%
- 121401
11.5%
M 118564
11.2%
o 85240
8.0%
d 81320
7.7%
n 81320
7.7%
r 81245
7.7%
S 72472
6.8%
i 40156
 
3.8%
Other values (9) 82826
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1060270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
174250
16.4%
e 121476
11.5%
- 121401
11.5%
M 118564
11.2%
o 85240
8.0%
d 81320
7.7%
n 81320
7.7%
r 81245
7.7%
S 72472
6.8%
i 40156
 
3.8%
Other values (9) 82826
7.8%

BTHRM_STYLE3
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing145740
Missing (%)80.0%
Memory size1.4 MiB
M - Modern
18538 
S - Semi-Modern
14078 
N - No Remodeling
1953 
L - Luxury
1933 

Length

Max length17
Median length10
Mean length12.302915
Min length10

Characters and Unicode

Total characters449081
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowM - Modern
4th rowS - Semi-Modern
5th rowN - No Remodeling

Common Values

ValueCountFrequency (%)
M - Modern 18538
 
10.2%
S - Semi-Modern 14078
 
7.7%
N - No Remodeling 1953
 
1.1%
L - Luxury 1933
 
1.1%
(Missing) 145740
80.0%

Length

2024-09-12T14:39:03.729561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:03.796964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
36502
32.7%
m 18538
16.6%
modern 18538
16.6%
s 14078
 
12.6%
semi-modern 14078
 
12.6%
n 1953
 
1.8%
no 1953
 
1.8%
remodeling 1953
 
1.8%
l 1933
 
1.7%
luxury 1933
 
1.7%

Most occurring characters

ValueCountFrequency (%)
74957
16.7%
M 51154
11.4%
e 50600
11.3%
- 50580
11.3%
o 36522
8.1%
d 34569
7.7%
n 34569
7.7%
r 34549
7.7%
S 28156
 
6.3%
i 16031
 
3.6%
Other values (9) 37394
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 449081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
74957
16.7%
M 51154
11.4%
e 50600
11.3%
- 50580
11.3%
o 36522
8.1%
d 34569
7.7%
n 34569
7.7%
r 34549
7.7%
S 28156
 
6.3%
i 16031
 
3.6%
Other values (9) 37394
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 449081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
74957
16.7%
M 51154
11.4%
e 50600
11.3%
- 50580
11.3%
o 36522
8.1%
d 34569
7.7%
n 34569
7.7%
r 34549
7.7%
S 28156
 
6.3%
i 16031
 
3.6%
Other values (9) 37394
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 449081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
74957
16.7%
M 51154
11.4%
e 50600
11.3%
- 50580
11.3%
o 36522
8.1%
d 34569
7.7%
n 34569
7.7%
r 34549
7.7%
S 28156
 
6.3%
i 16031
 
3.6%
Other values (9) 37394
8.3%

KITCHEN_TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing49555
Missing (%)27.2%
Memory size1.4 MiB
O - One Person
44878 
1F - 1 Full Eat In Kitchens
29285 
F - Full Eat In
24250 
2F - 2 Full Eat In Kitchens
16818 
3F - 3 Full Eat In Kitchens
11996 
Other values (5)
5460 

Length

Max length27
Median length15
Mean length20.065251
Min length8

Characters and Unicode

Total characters2662398
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3F - 3 Full Eat In Kitchens
2nd row3F - 3 Full Eat In Kitchens
3rd row3F - 3 Full Eat In Kitchens
4th row3F - 3 Full Eat In Kitchens
5th row2F - 2 Full Eat In Kitchens

Common Values

ValueCountFrequency (%)
O - One Person 44878
24.6%
1F - 1 Full Eat In Kitchens 29285
16.1%
F - Full Eat In 24250
13.3%
2F - 2 Full Eat In Kitchens 16818
 
9.2%
3F - 3 Full Eat In Kitchens 11996
 
6.6%
P - Pullman 2722
 
1.5%
0F - 0 Full Eat In Kitchens 2585
 
1.4%
N - None 115
 
0.1%
4F - 4 Full Eat In Kitchens 37
 
< 0.1%
5F - 5 Full Eat In Kitchens 1
 
< 0.1%
(Missing) 49555
27.2%

Length

2024-09-12T14:39:03.877937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:03.962188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
132687
18.1%
full 84972
11.6%
eat 84972
11.6%
in 84972
11.6%
kitchens 60722
8.3%
o 44878
 
6.1%
one 44878
 
6.1%
person 44878
 
6.1%
1f 29285
 
4.0%
1 29285
 
4.0%
Other values (15) 92798
12.6%

Most occurring characters

ValueCountFrequency (%)
601640
22.6%
n 238287
 
9.0%
l 175388
 
6.6%
F 169944
 
6.4%
e 150593
 
5.7%
t 145694
 
5.5%
- 132687
 
5.0%
s 105600
 
4.0%
O 89756
 
3.4%
u 87694
 
3.3%
Other values (18) 765115
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2662398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
601640
22.6%
n 238287
 
9.0%
l 175388
 
6.6%
F 169944
 
6.4%
e 150593
 
5.7%
t 145694
 
5.5%
- 132687
 
5.0%
s 105600
 
4.0%
O 89756
 
3.4%
u 87694
 
3.3%
Other values (18) 765115
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2662398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
601640
22.6%
n 238287
 
9.0%
l 175388
 
6.6%
F 169944
 
6.4%
e 150593
 
5.7%
t 145694
 
5.5%
- 132687
 
5.0%
s 105600
 
4.0%
O 89756
 
3.4%
u 87694
 
3.3%
Other values (18) 765115
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2662398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
601640
22.6%
n 238287
 
9.0%
l 175388
 
6.6%
F 169944
 
6.4%
e 150593
 
5.7%
t 145694
 
5.5%
- 132687
 
5.0%
s 105600
 
4.0%
O 89756
 
3.4%
u 87694
 
3.3%
Other values (18) 765115
28.7%

KITCHEN_STYLE1
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing49549
Missing (%)27.2%
Memory size1.4 MiB
M - Modern
63927 
S - Semi-Modern
54385 
L - Luxury
8532 
N - No Remodeling
 
5849

Length

Max length17
Median length10
Mean length12.357833
Min length10

Characters and Unicode

Total characters1639798
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowS - Semi-Modern
4th rowS - Semi-Modern
5th rowN - No Remodeling

Common Values

ValueCountFrequency (%)
M - Modern 63927
35.1%
S - Semi-Modern 54385
29.8%
L - Luxury 8532
 
4.7%
N - No Remodeling 5849
 
3.2%
(Missing) 49549
27.2%

Length

2024-09-12T14:39:04.064565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.133031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
132693
32.9%
m 63927
15.8%
modern 63927
15.8%
s 54385
13.5%
semi-modern 54385
13.5%
l 8532
 
2.1%
luxury 8532
 
2.1%
n 5849
 
1.4%
no 5849
 
1.4%
remodeling 5849
 
1.4%

Most occurring characters

ValueCountFrequency (%)
271235
16.5%
- 187078
11.4%
e 184395
11.2%
M 182239
11.1%
o 130010
7.9%
r 126844
7.7%
d 124161
7.6%
n 124161
7.6%
S 108770
6.6%
i 60234
 
3.7%
Other values (9) 140671
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
271235
16.5%
- 187078
11.4%
e 184395
11.2%
M 182239
11.1%
o 130010
7.9%
r 126844
7.7%
d 124161
7.6%
n 124161
7.6%
S 108770
6.6%
i 60234
 
3.7%
Other values (9) 140671
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
271235
16.5%
- 187078
11.4%
e 184395
11.2%
M 182239
11.1%
o 130010
7.9%
r 126844
7.7%
d 124161
7.6%
n 124161
7.6%
S 108770
6.6%
i 60234
 
3.7%
Other values (9) 140671
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
271235
16.5%
- 187078
11.4%
e 184395
11.2%
M 182239
11.1%
o 130010
7.9%
r 126844
7.7%
d 124161
7.6%
n 124161
7.6%
S 108770
6.6%
i 60234
 
3.7%
Other values (9) 140671
8.6%

KITCHEN_STYLE2
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing150994
Missing (%)82.9%
Memory size1.4 MiB
S - Semi-Modern
18164 
M - Modern
10127 
N - No Remodeling
2879 
L - Luxury
 
78

Length

Max length17
Median length15
Mean length13.551363
Min length10

Characters and Unicode

Total characters423453
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowS - Semi-Modern
4th rowS - Semi-Modern
5th rowN - No Remodeling

Common Values

ValueCountFrequency (%)
S - Semi-Modern 18164
 
10.0%
M - Modern 10127
 
5.6%
N - No Remodeling 2879
 
1.6%
L - Luxury 78
 
< 0.1%
(Missing) 150994
82.9%

Length

2024-09-12T14:39:04.206922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.272496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31248
32.3%
s 18164
18.8%
semi-modern 18164
18.8%
m 10127
 
10.5%
modern 10127
 
10.5%
n 2879
 
3.0%
no 2879
 
3.0%
remodeling 2879
 
3.0%
l 78
 
0.1%
luxury 78
 
0.1%

Most occurring characters

ValueCountFrequency (%)
65375
15.4%
e 52213
12.3%
- 49412
11.7%
M 38418
9.1%
S 36328
8.6%
o 34049
8.0%
d 31170
7.4%
n 31170
7.4%
r 28369
6.7%
i 21043
 
5.0%
Other values (9) 35906
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
65375
15.4%
e 52213
12.3%
- 49412
11.7%
M 38418
9.1%
S 36328
8.6%
o 34049
8.0%
d 31170
7.4%
n 31170
7.4%
r 28369
6.7%
i 21043
 
5.0%
Other values (9) 35906
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
65375
15.4%
e 52213
12.3%
- 49412
11.7%
M 38418
9.1%
S 36328
8.6%
o 34049
8.0%
d 31170
7.4%
n 31170
7.4%
r 28369
6.7%
i 21043
 
5.0%
Other values (9) 35906
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
65375
15.4%
e 52213
12.3%
- 49412
11.7%
M 38418
9.1%
S 36328
8.6%
o 34049
8.0%
d 31170
7.4%
n 31170
7.4%
r 28369
6.7%
i 21043
 
5.0%
Other values (9) 35906
8.5%

KITCHEN_STYLE3
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing168497
Missing (%)92.5%
Memory size1.4 MiB
S - Semi-Modern
7777 
M - Modern
4572 
N - No Remodeling
1367 
L - Luxury
 
29

Length

Max length17
Median length15
Mean length13.525209
Min length10

Characters and Unicode

Total characters185904
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS - Semi-Modern
2nd rowM - Modern
3rd rowS - Semi-Modern
4th rowS - Semi-Modern
5th rowM - Modern

Common Values

ValueCountFrequency (%)
S - Semi-Modern 7777
 
4.3%
M - Modern 4572
 
2.5%
N - No Remodeling 1367
 
0.8%
L - Luxury 29
 
< 0.1%
(Missing) 168497
92.5%

Length

2024-09-12T14:39:04.349512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.413747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
13745
32.3%
s 7777
18.3%
semi-modern 7777
18.3%
m 4572
 
10.7%
modern 4572
 
10.7%
n 1367
 
3.2%
no 1367
 
3.2%
remodeling 1367
 
3.2%
l 29
 
0.1%
luxury 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
28857
15.5%
e 22860
12.3%
- 21522
11.6%
M 16921
9.1%
S 15554
8.4%
o 15083
8.1%
d 13716
7.4%
n 13716
7.4%
r 12378
6.7%
i 9144
 
4.9%
Other values (9) 16153
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 185904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28857
15.5%
e 22860
12.3%
- 21522
11.6%
M 16921
9.1%
S 15554
8.4%
o 15083
8.1%
d 13716
7.4%
n 13716
7.4%
r 12378
6.7%
i 9144
 
4.9%
Other values (9) 16153
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 185904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28857
15.5%
e 22860
12.3%
- 21522
11.6%
M 16921
9.1%
S 15554
8.4%
o 15083
8.1%
d 13716
7.4%
n 13716
7.4%
r 12378
6.7%
i 9144
 
4.9%
Other values (9) 16153
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 185904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28857
15.5%
e 22860
12.3%
- 21522
11.6%
M 16921
9.1%
S 15554
8.4%
o 15083
8.1%
d 13716
7.4%
n 13716
7.4%
r 12378
6.7%
i 9144
 
4.9%
Other values (9) 16153
8.7%

HEAT_TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing48242
Missing (%)26.5%
Memory size1.4 MiB
W - Ht Water/Steam
71163 
F - Forced Hot Air
51379 
E - Electric
 
6023
P - Heat Pump
 
4654
S - Space Heat
 
652
Other values (2)
 
129

Length

Max length18
Median length18
Mean length17.527873
Min length8

Characters and Unicode

Total characters2348735
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW - Ht Water/Steam
2nd rowF - Forced Hot Air
3rd rowS - Space Heat
4th rowW - Ht Water/Steam
5th rowW - Ht Water/Steam

Common Values

ValueCountFrequency (%)
W - Ht Water/Steam 71163
39.0%
F - Forced Hot Air 51379
28.2%
E - Electric 6023
 
3.3%
P - Heat Pump 4654
 
2.6%
S - Space Heat 652
 
0.4%
N - None 88
 
< 0.1%
O - Other 41
 
< 0.1%
(Missing) 48242
26.5%

Length

2024-09-12T14:39:04.488084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.555869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
134000
23.1%
w 71163
12.2%
ht 71163
12.2%
water/steam 71163
12.2%
f 51379
 
8.8%
forced 51379
 
8.8%
hot 51379
 
8.8%
air 51379
 
8.8%
electric 6023
 
1.0%
e 6023
 
1.0%
Other values (9) 16176
 
2.8%

Most occurring characters

ValueCountFrequency (%)
447227
19.0%
t 276238
11.8%
e 205815
8.8%
r 179985
 
7.7%
a 148284
 
6.3%
W 142326
 
6.1%
- 134000
 
5.7%
H 127848
 
5.4%
o 102846
 
4.4%
F 102758
 
4.4%
Other values (16) 481408
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2348735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
447227
19.0%
t 276238
11.8%
e 205815
8.8%
r 179985
 
7.7%
a 148284
 
6.3%
W 142326
 
6.1%
- 134000
 
5.7%
H 127848
 
5.4%
o 102846
 
4.4%
F 102758
 
4.4%
Other values (16) 481408
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2348735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
447227
19.0%
t 276238
11.8%
e 205815
8.8%
r 179985
 
7.7%
a 148284
 
6.3%
W 142326
 
6.1%
- 134000
 
5.7%
H 127848
 
5.4%
o 102846
 
4.4%
F 102758
 
4.4%
Other values (16) 481408
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2348735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
447227
19.0%
t 276238
11.8%
e 205815
8.8%
r 179985
 
7.7%
a 148284
 
6.3%
W 142326
 
6.1%
- 134000
 
5.7%
H 127848
 
5.4%
o 102846
 
4.4%
F 102758
 
4.4%
Other values (16) 481408
20.5%

HEAT_SYSTEM
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing110013
Missing (%)60.4%
Memory size1.4 MiB
I - Indiv. Cntrl
43889 
Y - Self Contained
19271 
C - Common
8042 
N - None
 
969
1
 
58

Length

Max length18
Median length16
Mean length15.746196
Min length1

Characters and Unicode

Total characters1137332
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI - Indiv. Cntrl
2nd rowI - Indiv. Cntrl
3rd rowI - Indiv. Cntrl
4th rowI - Indiv. Cntrl
5th rowY - Self Contained

Common Values

ValueCountFrequency (%)
I - Indiv. Cntrl 43889
 
24.1%
Y - Self Contained 19271
 
10.6%
C - Common 8042
 
4.4%
N - None 969
 
0.5%
1 58
 
< 0.1%
(Missing) 110013
60.4%

Length

2024-09-12T14:39:04.639618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.711188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
72171
25.8%
i 43889
15.7%
indiv 43889
15.7%
cntrl 43889
15.7%
y 19271
 
6.9%
self 19271
 
6.9%
contained 19271
 
6.9%
c 8042
 
2.9%
common 8042
 
2.9%
n 969
 
0.3%
Other values (2) 1027
 
0.4%

Most occurring characters

ValueCountFrequency (%)
207502
18.2%
n 135331
11.9%
I 87778
 
7.7%
C 79244
 
7.0%
- 72171
 
6.3%
t 63160
 
5.6%
d 63160
 
5.6%
i 63160
 
5.6%
l 63160
 
5.6%
r 43889
 
3.9%
Other values (11) 258777
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
207502
18.2%
n 135331
11.9%
I 87778
 
7.7%
C 79244
 
7.0%
- 72171
 
6.3%
t 63160
 
5.6%
d 63160
 
5.6%
i 63160
 
5.6%
l 63160
 
5.6%
r 43889
 
3.9%
Other values (11) 258777
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
207502
18.2%
n 135331
11.9%
I 87778
 
7.7%
C 79244
 
7.0%
- 72171
 
6.3%
t 63160
 
5.6%
d 63160
 
5.6%
i 63160
 
5.6%
l 63160
 
5.6%
r 43889
 
3.9%
Other values (11) 258777
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
207502
18.2%
n 135331
11.9%
I 87778
 
7.7%
C 79244
 
7.0%
- 72171
 
6.3%
t 63160
 
5.6%
d 63160
 
5.6%
i 63160
 
5.6%
l 63160
 
5.6%
r 43889
 
3.9%
Other values (11) 258777
22.8%

AC_TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing48272
Missing (%)26.5%
Memory size1.4 MiB
N - None
78655 
C - Central AC
53937 
D - Ductless AC
 
1377
Y - Yes
 
1

Length

Max length15
Median length8
Mean length10.487572
Min length7

Characters and Unicode

Total characters1405020
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowN - None
2nd rowC - Central AC
3rd rowN - None
4th rowN - None
5th rowN - None

Common Values

ValueCountFrequency (%)
N - None 78655
43.2%
C - Central AC 53937
29.6%
D - Ductless AC 1377
 
0.8%
Y - Yes 1
 
< 0.1%
(Missing) 48272
26.5%

Length

2024-09-12T14:39:04.793513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:04.860178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
133970
29.3%
n 78655
17.2%
none 78655
17.2%
ac 55314
12.1%
c 53937
11.8%
central 53937
11.8%
d 1377
 
0.3%
ductless 1377
 
0.3%
y 1
 
< 0.1%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
323254
23.0%
C 163188
11.6%
N 157310
11.2%
- 133970
9.5%
e 133970
9.5%
n 132592
9.4%
o 78655
 
5.6%
l 55314
 
3.9%
t 55314
 
3.9%
A 55314
 
3.9%
Other values (7) 116139
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1405020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
323254
23.0%
C 163188
11.6%
N 157310
11.2%
- 133970
9.5%
e 133970
9.5%
n 132592
9.4%
o 78655
 
5.6%
l 55314
 
3.9%
t 55314
 
3.9%
A 55314
 
3.9%
Other values (7) 116139
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1405020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
323254
23.0%
C 163188
11.6%
N 157310
11.2%
- 133970
9.5%
e 133970
9.5%
n 132592
9.4%
o 78655
 
5.6%
l 55314
 
3.9%
t 55314
 
3.9%
A 55314
 
3.9%
Other values (7) 116139
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1405020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
323254
23.0%
C 163188
11.6%
N 157310
11.2%
- 133970
9.5%
e 133970
9.5%
n 132592
9.4%
o 78655
 
5.6%
l 55314
 
3.9%
t 55314
 
3.9%
A 55314
 
3.9%
Other values (7) 116139
 
8.3%

FIREPLACES
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing49534
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean0.34416162
Minimum0
Maximum12
Zeros96980
Zeros (%)53.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:04.924829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.68600397
Coefficient of variation (CV)1.9932611
Kurtosis22.273269
Mean0.34416162
Median Absolute Deviation (MAD)0
Skewness3.4407165
Sum45673
Variance0.47060145
MonotonicityNot monotonic
2024-09-12T14:39:04.992470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 96980
53.2%
1 28983
 
15.9%
2 5085
 
2.8%
3 917
 
0.5%
4 359
 
0.2%
5 167
 
0.1%
6 114
 
0.1%
7 48
 
< 0.1%
8 34
 
< 0.1%
9 12
 
< 0.1%
Other values (3) 9
 
< 0.1%
(Missing) 49534
27.2%
ValueCountFrequency (%)
0 96980
53.2%
1 28983
 
15.9%
2 5085
 
2.8%
3 917
 
0.5%
4 359
 
0.2%
5 167
 
0.1%
6 114
 
0.1%
7 48
 
< 0.1%
8 34
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
11 4
 
< 0.1%
10 3
 
< 0.1%
9 12
 
< 0.1%
8 34
 
< 0.1%
7 48
 
< 0.1%
6 114
 
0.1%
5 167
 
0.1%
4 359
 
0.2%
3 917
0.5%

ORIENTATION
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing110268
Missing (%)60.5%
Memory size1.4 MiB
T - Through
37600 
F - Front/Street
17990 
A - Rear Above
10386 
M - Middle
 
2724
C - Courtyard
 
1761
Other values (2)
 
1513

Length

Max length16
Median length11
Mean length12.732112
Min length7

Characters and Unicode

Total characters916381
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT - Through
2nd rowT - Through
3rd rowT - Through
4th rowT - Through
5th rowT - Through

Common Values

ValueCountFrequency (%)
T - Through 37600
 
20.6%
F - Front/Street 17990
 
9.9%
A - Rear Above 10386
 
5.7%
M - Middle 2724
 
1.5%
C - Courtyard 1761
 
1.0%
B - Rear Below 1259
 
0.7%
E - End 254
 
0.1%
(Missing) 110268
60.5%

Length

2024-09-12T14:39:05.061868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:05.221057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
71974
31.6%
t 37600
16.5%
through 37600
16.5%
f 17990
 
7.9%
front/street 17990
 
7.9%
rear 11645
 
5.1%
a 10386
 
4.6%
above 10386
 
4.6%
m 2724
 
1.2%
middle 2724
 
1.2%
Other values (6) 6548
 
2.9%

Most occurring characters

ValueCountFrequency (%)
155593
17.0%
r 88747
9.7%
T 75200
8.2%
h 75200
8.2%
- 71974
 
7.9%
o 68996
 
7.5%
e 61994
 
6.8%
t 55731
 
6.1%
u 39361
 
4.3%
g 37600
 
4.1%
Other values (18) 185985
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 916381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
155593
17.0%
r 88747
9.7%
T 75200
8.2%
h 75200
8.2%
- 71974
 
7.9%
o 68996
 
7.5%
e 61994
 
6.8%
t 55731
 
6.1%
u 39361
 
4.3%
g 37600
 
4.1%
Other values (18) 185985
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 916381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
155593
17.0%
r 88747
9.7%
T 75200
8.2%
h 75200
8.2%
- 71974
 
7.9%
o 68996
 
7.5%
e 61994
 
6.8%
t 55731
 
6.1%
u 39361
 
4.3%
g 37600
 
4.1%
Other values (18) 185985
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 916381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
155593
17.0%
r 88747
9.7%
T 75200
8.2%
h 75200
8.2%
- 71974
 
7.9%
o 68996
 
7.5%
e 61994
 
6.8%
t 55731
 
6.1%
u 39361
 
4.3%
g 37600
 
4.1%
Other values (18) 185985
20.3%

NUM_PARKING
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing48623
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean1.3289427
Minimum0
Maximum210
Zeros58524
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-09-12T14:39:05.298757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum210
Range210
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.411295
Coefficient of variation (CV)1.8144461
Kurtosis1663.0013
Mean1.3289427
Median Absolute Deviation (MAD)1
Skewness29.679935
Sum177572
Variance5.8143437
MonotonicityNot monotonic
2024-09-12T14:39:05.367800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 58524
32.1%
1 29227
16.0%
2 21830
 
12.0%
3 8748
 
4.8%
4 8222
 
4.5%
6 3011
 
1.7%
5 2636
 
1.4%
8 631
 
0.3%
7 554
 
0.3%
10 95
 
0.1%
Other values (13) 141
 
0.1%
(Missing) 48623
26.7%
ValueCountFrequency (%)
0 58524
32.1%
1 29227
16.0%
2 21830
 
12.0%
3 8748
 
4.8%
4 8222
 
4.5%
5 2636
 
1.4%
6 3011
 
1.7%
7 554
 
0.3%
8 631
 
0.3%
9 88
 
< 0.1%
ValueCountFrequency (%)
210 1
 
< 0.1%
125 24
< 0.1%
56 1
 
< 0.1%
22 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
14 5
 
< 0.1%
13 2
 
< 0.1%

PROP_VIEW
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing46953
Missing (%)25.8%
Memory size1.4 MiB
A - Average
110895 
G - Good
13086 
F - Fair
 
6033
E - Excellent
 
4405
P - Poor
 
525

Length

Max length13
Median length11
Mean length10.629519
Min length8

Characters and Unicode

Total characters1438057
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA - Average
2nd rowA - Average
3rd rowA - Average
4th rowA - Average
5th rowA - Average

Common Values

ValueCountFrequency (%)
A - Average 110895
60.9%
G - Good 13086
 
7.2%
F - Fair 6033
 
3.3%
E - Excellent 4405
 
2.4%
P - Poor 525
 
0.3%
S - Special 345
 
0.2%
(Missing) 46953
25.8%

Length

2024-09-12T14:39:05.439296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:05.505835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
135289
33.3%
a 110895
27.3%
average 110895
27.3%
g 13086
 
3.2%
good 13086
 
3.2%
f 6033
 
1.5%
fair 6033
 
1.5%
e 4405
 
1.1%
excellent 4405
 
1.1%
p 525
 
0.1%
Other values (3) 1215
 
0.3%

Most occurring characters

ValueCountFrequency (%)
270578
18.8%
e 230945
16.1%
A 221790
15.4%
- 135289
9.4%
r 117453
8.2%
a 117273
8.2%
v 110895
7.7%
g 110895
7.7%
o 27222
 
1.9%
G 26172
 
1.8%
Other values (12) 69545
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1438057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
270578
18.8%
e 230945
16.1%
A 221790
15.4%
- 135289
9.4%
r 117453
8.2%
a 117273
8.2%
v 110895
7.7%
g 110895
7.7%
o 27222
 
1.9%
G 26172
 
1.8%
Other values (12) 69545
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1438057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
270578
18.8%
e 230945
16.1%
A 221790
15.4%
- 135289
9.4%
r 117453
8.2%
a 117273
8.2%
v 110895
7.7%
g 110895
7.7%
o 27222
 
1.9%
G 26172
 
1.8%
Other values (12) 69545
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1438057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
270578
18.8%
e 230945
16.1%
A 221790
15.4%
- 135289
9.4%
r 117453
8.2%
a 117273
8.2%
v 110895
7.7%
g 110895
7.7%
o 27222
 
1.9%
G 26172
 
1.8%
Other values (12) 69545
 
4.8%

CORNER_UNIT
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing110271
Missing (%)60.5%
Memory size1.4 MiB
N - No
58140 
Y - Yes
13831 

Length

Max length7
Median length6
Mean length6.1921746
Min length6

Characters and Unicode

Total characters445657
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN - No
2nd rowN - No
3rd rowN - No
4th rowN - No
5th rowN - No

Common Values

ValueCountFrequency (%)
N - No 58140
31.9%
Y - Yes 13831
 
7.6%
(Missing) 110271
60.5%

Length

2024-09-12T14:39:05.581830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-12T14:39:05.643387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
71971
33.3%
n 58140
26.9%
no 58140
26.9%
y 13831
 
6.4%
yes 13831
 
6.4%

Most occurring characters

ValueCountFrequency (%)
143942
32.3%
N 116280
26.1%
- 71971
16.1%
o 58140
13.0%
Y 27662
 
6.2%
e 13831
 
3.1%
s 13831
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 445657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
143942
32.3%
N 116280
26.1%
- 71971
16.1%
o 58140
13.0%
Y 27662
 
6.2%
e 13831
 
3.1%
s 13831
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 445657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
143942
32.3%
N 116280
26.1%
- 71971
16.1%
o 58140
13.0%
Y 27662
 
6.2%
e 13831
 
3.1%
s 13831
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 445657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
143942
32.3%
N 116280
26.1%
- 71971
16.1%
o 58140
13.0%
Y 27662
 
6.2%
e 13831
 
3.1%
s 13831
 
3.1%

Interactions

2024-09-12T14:38:42.062466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:06.978099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.943612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.582798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.070698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.682147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.228655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.818304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.471945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.016023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.589052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.832009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.146616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.418452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.037129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.554870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.286170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.794669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.369293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.849626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.500558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.030190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.432819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.134340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.094317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.016301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.664764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.142864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.756635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.300288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.982710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.545085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.087011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.653045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.896781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.213103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.495815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.112607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.632176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.360158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.867114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.441657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.926160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.574136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.105580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.504289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.193514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.231918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.075679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.724060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.204360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.817896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.362082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.043864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.608370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.151736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.712071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.954049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.271590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.562016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.174373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.696680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.425946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.927085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.501971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.993197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.632980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.168255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.569090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.255655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.347553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.138022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.787506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.269458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.885808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.433318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.109637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.677934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.213555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.770552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.013047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.329978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.629404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.240907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.765499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.490849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.995920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.572263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.063891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.704571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.238803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.635670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.319696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.478323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.204840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.850363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.333696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.952208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.496919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.175891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.746707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.360076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.830647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.070760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.390398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.697087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.306337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.833896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.556397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:32.063557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.638406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.130099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.777835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.307485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.705517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.383226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.550064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.272645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.912981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.401346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.019473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.563254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.250307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.813547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:20.422280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.889057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.129057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.449653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.765167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.374978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.905061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.623942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:32.129742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.704785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.199193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.876572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:38.377803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.774972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:42.445645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:07.616079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.336251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.982328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.464664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.084566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-12T14:38:42.996105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.255698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:09.930381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.543711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.052831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.674836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.288532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.920511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.468799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.041909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.461939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.777938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.040844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.486352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:27.996756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:29.660254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.235314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:32.840376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.312569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.820456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.491189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:39.325238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.481665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.069630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.333346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.002878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.607758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.130431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.749368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.361207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:17.994088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.541397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.105594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.510113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.823426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.086658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.561064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.073647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:29.733023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.308708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:32.911882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.385248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.896160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.563056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:39.443934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.558431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.131191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.402433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.068021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.675430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.197515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.819858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.422898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.060213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.608093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.175389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.554929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.871003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.132942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.626779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.141470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:29.799152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.373394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:32.971258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.447304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:35.961535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.627700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:39.514834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.654275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.193591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.527307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.254530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.741517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.262691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.884558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.486147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.126258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.671313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.242584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.600238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.915722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.179924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.690702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.205319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:29.864794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.440061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.034266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.513202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.131000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.690721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:39.672969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.717985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.259371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.618089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.325601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.809435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.333587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:14.954300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.555360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.196501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.739010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.314739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.647144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:23.961187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.224845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.762569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.277251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:29.936929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.511970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.103527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.583215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.202819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.761385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.024119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.788678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.323347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.691398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.390435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.876799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.480823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.017992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.619164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.264275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.804632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.386727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.692893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.007930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.271663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.829417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.344243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.004917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.579924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.169010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.647625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.294232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.825117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.177888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.855662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.395041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.803041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.458248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:11.943314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.550543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.091069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.689089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.337958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.878544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.457471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.739984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.052829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.316830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.905456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.420543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.137745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.652241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.237324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.718296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.364926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.897981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.282320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.927342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:43.460861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:08.875346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:10.524551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:12.008444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:13.617789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:15.156992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:16.755175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:18.407684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:19.951459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:21.524551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:22.784886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:24.100534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:25.362505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:26.972630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:28.486562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:30.222089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:31.725202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:33.304758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:34.784041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:36.432793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:37.965396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:40.365245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-12T14:38:41.997431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-12T14:39:05.732712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AC_TYPEBDRM_CONDBED_RMSBTHRM_STYLE1BTHRM_STYLE2BTHRM_STYLE3CD_FLOORCITYCM_IDCOM_UNITSCORNER_UNITEXT_CONDEXT_FNISHEDFIREPLACESFULL_BTHGIS_IDGROSS_AREAHEAT_SYSTEMHEAT_TYPEHLF_BTHINT_CONDINT_WALLKITCHENSKITCHEN_STYLE1KITCHEN_STYLE2KITCHEN_STYLE3KITCHEN_TYPELIVING_AREALULUCNUM_BLDGSNUM_PARKINGORIENTATIONOVERALL_CONDOWN_OCCPIDPROP_VIEWRC_UNITSRES_FLOORRES_UNITSROOF_COVERROOF_STRUCTURESTRUCTURE_CLASSST_NUMTT_RMSYR_BUILTYR_REMODELZIP_CODE_id
AC_TYPE1.0000.0890.1820.2770.3590.3740.1430.2080.1901.0000.0480.3720.3040.0360.0690.2031.0000.1930.3420.0700.3190.1160.2420.2750.2500.2510.2661.0000.2450.0160.0050.0000.0770.2080.0300.2030.1711.0000.0001.0000.2200.1740.1460.0320.1950.0000.0060.1190.213
BDRM_COND0.0891.0000.0260.1910.2140.2120.2470.1480.1270.0000.1420.1620.2760.0510.0640.1271.0000.0720.1010.0690.1450.1890.0000.1880.4791.0000.0941.0000.0040.0090.0001.0000.1010.1810.0740.1270.1880.0000.0220.0000.0890.0550.3640.0350.1270.0000.0000.1270.155
BED_RMS0.1820.0261.0000.1070.1900.237-0.1890.2060.150NaN0.0270.1580.2000.0340.6590.2610.8980.0510.1080.1710.1420.0440.6960.1130.0620.0830.3550.8730.3510.2820.0030.4490.1250.0440.2390.2610.100NaN0.742NaN0.2110.2210.138-0.1460.940-0.1470.2770.1230.261
BTHRM_STYLE10.2770.1910.1071.0000.7560.6680.2310.2340.1950.0000.1190.3500.3380.0800.0570.1951.0000.1560.2180.0880.5250.3550.1180.7690.5420.5520.1771.0000.1681.0000.0150.0120.1250.3300.0570.1950.2280.0000.0120.0000.1980.1380.3070.0600.1220.0000.0060.1590.218
BTHRM_STYLE20.3590.2140.1900.7561.0000.8200.2650.2790.1830.0000.1450.3740.3720.0380.0630.2181.0000.1240.2200.0550.4990.3510.1850.6580.6780.6380.2611.0000.2601.0000.0210.0000.1460.3250.0840.2180.2520.0000.0120.0000.2150.1870.3130.0390.2070.0000.0000.2090.258
BTHRM_STYLE30.3740.2120.2370.6680.8201.0000.2940.2980.2230.0000.2160.4070.3850.0540.1300.2091.0000.1260.2160.1740.5060.3570.2550.6130.5890.7130.3091.0000.3051.0000.0340.0000.2080.3460.0800.2090.2940.0000.0140.0000.1950.1380.4130.0400.2711.0000.0080.2390.273
CD_FLOOR0.1430.247-0.1890.2310.2650.2941.0000.114-0.260NaN0.1690.2050.211-0.1030.032-0.260-0.0610.2130.061-0.0770.1620.315-0.0100.2201.0001.0000.079-0.0511.000NaN0.004-0.1020.1210.1690.097-0.2600.403NaN-0.382NaN0.1810.1240.3890.021-0.2160.184-0.070-0.232-0.260
CITY0.2080.1480.2060.2340.2790.2980.1141.0000.6600.0000.1500.1950.1960.0450.0780.6810.0100.1540.1370.0740.2170.1660.1830.2290.1260.0990.2520.0100.1750.0720.0060.0260.1690.0800.2610.6810.1770.0000.0210.0840.2650.2650.2590.1760.2200.0000.0210.6940.752
CM_ID0.1900.1270.1500.1950.1830.223-0.2600.6601.000-0.1250.1430.1470.227-0.1040.0111.000-0.0080.1620.145-0.0140.1940.1190.1350.1950.5610.2170.140-0.0140.078-0.0480.0000.2300.1350.0990.1531.0000.152-0.0040.028-0.1690.2370.2190.3410.0660.1350.101-0.0230.5821.000
COM_UNITS1.0000.000NaN0.0000.0000.000NaN0.000-0.1251.0000.0000.0160.000NaNNaN-0.1250.3071.0001.000NaN0.0000.000NaN0.0000.0000.0000.000NaN1.000NaN0.000NaN0.0000.0001.000-0.1251.0000.0930.2480.0730.0000.0001.0000.145NaN0.105NaN-0.137-0.125
CORNER_UNIT0.0480.1420.0270.1190.1450.2160.1690.1500.1430.0001.0000.1360.2080.0440.0500.1431.0000.0630.0340.0420.1270.0940.0000.1120.4100.5240.0741.0001.0001.0000.0001.0000.2950.0980.0360.1430.1690.0000.0000.0000.1440.1460.0460.0620.0850.0000.0000.1010.169
EXT_COND0.3720.1620.1580.3500.3740.4070.2050.1950.1470.0160.1361.0000.3850.0380.0580.1840.0000.0950.2090.0380.4280.2740.1630.3430.2220.2110.2270.0000.2330.0470.0110.0370.1270.3980.0700.1840.2050.0000.0300.0770.2190.1700.2840.0440.1660.0090.0000.0990.193
EXT_FNISHED0.3040.2760.2000.3380.3720.3850.2110.1960.2270.0000.2080.3851.0000.0350.0870.2290.1250.1780.1550.0930.3050.3540.4630.3290.1450.1360.2360.1300.3310.3440.0000.5840.2130.2030.3340.2290.2110.0000.1680.1310.3030.2590.5960.0750.2180.0000.0360.2180.244
FIREPLACES0.0360.0510.0340.0800.0380.054-0.1030.045-0.104NaN0.0440.0380.0351.0000.0440.0180.1030.0320.0210.2530.0480.129-0.1810.0870.1230.1170.0440.1030.070-0.2830.0020.1250.0490.0910.0380.0180.029NaN0.057NaN0.0820.0610.0990.0000.054-0.0620.104-0.0110.018
FULL_BTH0.0690.0640.6590.0570.0630.1300.0320.0780.011NaN0.0500.0580.0870.0441.0000.0620.3110.0240.0550.1930.0470.0440.8590.0580.1500.1770.2450.3430.250-0.2560.0170.2330.0300.0470.0450.0620.040NaN0.433NaN0.0190.0110.065-0.1120.664-0.0600.2330.0520.062
GIS_ID0.2030.1270.2610.1950.2180.209-0.2600.6811.000-0.1250.1430.1840.2290.0180.0621.0000.1940.1620.1320.0810.2020.1450.1260.1910.1330.0940.2080.1160.191-0.1250.0020.4380.1350.0700.1971.0000.161-0.0040.034-0.1690.2620.2580.247-0.0580.2550.150-0.0230.6081.000
GROSS_AREA1.0001.0000.8981.0001.0001.000-0.0610.010-0.0080.3071.0000.0000.1250.1030.3110.1941.0001.0001.0000.1411.0001.0000.2451.0001.0001.0001.0000.9700.0520.3030.0000.5191.0000.0510.0250.1940.0060.1430.7750.2520.0020.0000.102-0.0890.925-0.1040.2430.0700.194
HEAT_SYSTEM0.1930.0720.0510.1560.1240.1260.2130.1540.1621.0000.0630.0950.1780.0320.0240.1621.0001.0000.2320.0630.1090.1020.0000.1370.2340.0000.0411.0000.0200.0200.0131.0000.0900.1160.1220.1620.0831.0000.0001.0000.1660.0770.0890.0710.0840.0000.0000.0670.176
HEAT_TYPE0.3420.1010.1080.2180.2200.2160.0610.1370.1451.0000.0340.2090.1550.0210.0550.1321.0000.2321.0000.0350.2120.0880.1110.2120.1010.0990.1551.0000.1430.0170.0000.0000.0560.1400.0650.1320.0991.0000.0051.0000.1390.0890.3740.0350.1240.0000.0000.0830.131
HLF_BTH0.0700.0690.1710.0880.0550.174-0.0770.074-0.014NaN0.0420.0380.0930.2530.1930.0810.1410.0630.0351.0000.0500.0810.1360.0890.1150.1550.1550.1430.180-0.3710.0310.2200.0560.0690.2680.0810.046NaN0.220NaN0.1090.1170.103-0.0940.1780.0910.1900.0680.081
INT_COND0.3190.1450.1420.5250.4990.5060.1620.2170.1940.0000.1270.4280.3050.0480.0470.2021.0000.1090.2120.0501.0000.2940.1420.5330.4120.4180.2151.0000.2020.0120.0090.0000.1160.4780.0440.2020.1940.0000.0090.0000.1980.1600.1780.0460.1520.0060.0120.1210.215
INT_WALL0.1160.1890.0440.3550.3510.3570.3150.1660.1190.0000.0940.2740.3540.1290.0440.1451.0000.1020.0880.0810.2941.0000.0990.3660.1680.1550.0991.0000.2290.1440.0160.0000.1040.2600.0570.1450.2230.0000.0080.0000.1650.1150.3380.0280.0410.0000.0000.1180.166
KITCHENS0.2420.0000.6960.1180.1850.255-0.0100.1830.135NaN0.0000.1630.463-0.1810.8590.1260.2450.0000.1110.1360.1420.0991.0000.1330.0340.0480.7770.2220.752-0.2100.0070.2230.0000.0610.4750.1260.078NaN0.417NaN0.1790.1720.225-0.1250.721-0.1650.0660.0970.126
KITCHEN_STYLE10.2750.1880.1130.7690.6580.6130.2200.2290.1950.0000.1120.3430.3290.0870.0580.1911.0000.1370.2120.0890.5330.3660.1331.0000.6380.6460.1921.0000.1721.0000.0140.0000.1250.3190.0570.1910.2150.0000.0110.0000.1910.1360.2980.0510.1290.0000.0060.1560.218
KITCHEN_STYLE20.2500.4790.0620.5420.6780.5891.0000.1260.5610.0000.4100.2220.1450.1230.1500.1331.0000.2340.1010.1150.4120.1680.0340.6381.0000.8120.0871.0000.0681.0000.0060.0150.2830.3580.1010.1330.0610.0001.0000.0000.0880.0660.1280.0070.0291.0000.0650.0660.138
KITCHEN_STYLE30.2511.0000.0830.5520.6380.7131.0000.0990.2170.0000.5240.2110.1360.1170.1770.0941.0000.0000.0990.1550.4180.1550.0480.6460.8121.0000.0951.0000.0831.0000.0090.0211.0000.3570.1480.0940.0640.0001.0000.0000.0900.0230.0300.0160.0501.0000.0000.0410.105
KITCHEN_TYPE0.2660.0940.3550.1770.2610.3090.0790.2520.1400.0000.0740.2270.2360.0440.2450.2081.0000.0410.1550.1550.2150.0990.7770.1920.0870.0951.0001.0000.9501.0000.0030.0030.1400.0760.3040.2080.1410.0000.0000.0000.2720.2890.1720.0490.4300.0000.0080.1660.228
LIVING_AREA1.0001.0000.8731.0001.0001.000-0.0510.010-0.014NaN1.0000.0000.1300.1030.3430.1160.9701.0001.0000.1431.0001.0000.2221.0001.0001.0001.0001.0000.0490.3690.0000.4491.0000.0470.0230.1160.006NaN0.790NaN0.0000.0000.096-0.0650.900-0.1040.2480.0040.116
LU0.2450.0040.3510.1680.2600.3051.0000.1750.0781.0001.0000.2330.3310.0700.2500.1910.0520.0200.1430.1800.2020.2290.7520.1720.0680.0830.9500.0491.0000.8380.0120.3301.0000.0950.5700.1910.1221.0000.0571.0000.2680.2790.3070.0670.4080.0000.0330.1400.211
LUC0.0160.0090.2821.0001.0001.000NaN0.072-0.048NaN1.0000.0470.344-0.283-0.256-0.1250.3030.0200.017-0.3710.0120.144-0.2101.0001.0001.0001.0000.3690.8381.0000.005-0.0831.0000.0540.416-0.1250.016NaN0.459NaN0.0380.0440.1910.0730.294-0.045-0.038-0.134-0.125
NUM_BLDGS0.0050.0000.0030.0150.0210.0340.0040.0060.0000.0000.0000.0110.0000.0020.0170.0020.0000.0130.0000.0310.0090.0160.0070.0140.0060.0090.0030.0000.0120.0051.0000.0000.0000.0120.0040.0020.0050.0000.0000.0000.0030.0000.0000.0000.0030.0000.0000.0030.004
NUM_PARKING0.0001.0000.4490.0120.0000.000-0.1020.0260.230NaN1.0000.0370.5840.1250.2330.4380.5191.0000.0000.2200.0000.0000.2230.0000.0150.0210.0030.4490.330-0.0830.0001.0001.0000.0160.0160.4380.000NaN0.311NaN0.0130.0100.039-0.1030.4570.1420.1660.2860.438
ORIENTATION0.0770.1010.1250.1250.1460.2080.1210.1690.1350.0000.2950.1270.2130.0490.0300.1351.0000.0900.0560.0560.1160.1040.0000.1250.2831.0000.1401.0001.0001.0000.0001.0001.0000.0730.1680.1350.1620.0000.0310.0000.1600.1490.3700.0880.1720.0000.0000.1570.162
OVERALL_COND0.2080.1810.0440.3300.3250.3460.1690.0800.0990.0000.0980.3980.2030.0910.0470.0700.0510.1160.1400.0690.4780.2600.0610.3190.3580.3570.0760.0470.0950.0540.0120.0160.0731.0000.0580.0700.1390.0000.0310.0590.1700.1240.2860.0210.0550.0000.0340.0910.076
OWN_OCC0.0300.0740.2390.0570.0840.0800.0970.2610.1531.0000.0360.0700.3340.0380.0450.1970.0250.1220.0650.2680.0440.0570.4750.0570.1010.1480.3040.0230.5700.4160.0040.0160.1680.0581.0000.1970.0911.0000.0451.0000.2600.2650.1990.0860.2920.0000.0130.1270.255
PID0.2030.1270.2610.1950.2180.209-0.2600.6811.000-0.1250.1430.1840.2290.0180.0621.0000.1940.1620.1320.0810.2020.1450.1260.1910.1330.0940.2080.1160.191-0.1250.0020.4380.1350.0700.1971.0000.161-0.0040.034-0.1690.2620.2580.247-0.0580.2550.150-0.0230.6081.000
PROP_VIEW0.1710.1880.1000.2280.2520.2940.4030.1770.1521.0000.1690.2050.2110.0290.0400.1610.0060.0830.0990.0460.1940.2230.0780.2150.0610.0640.1410.0060.1220.0160.0050.0000.1620.1390.0910.1611.0001.0000.0100.0770.1570.1310.3240.0290.1050.0000.0000.1550.185
RC_UNITS1.0000.000NaN0.0000.0000.000NaN0.000-0.0040.0930.0000.0000.000NaNNaN-0.0040.1431.0001.000NaN0.0000.000NaN0.0000.0000.0000.000NaN1.000NaN0.000NaN0.0000.0001.000-0.0041.0001.0000.075-0.0350.0000.0001.0000.055NaN0.043NaN-0.008-0.004
RES_FLOOR0.0000.0220.7420.0120.0120.014-0.3820.0210.0280.2480.0000.0300.1680.0570.4330.0340.7750.0000.0050.2200.0090.0080.4170.0111.0001.0000.0000.7900.0570.4590.0000.3110.0310.0310.0450.0340.0100.0751.0000.4730.0210.0170.122-0.0880.767-0.1610.197-0.0240.034
RES_UNITS1.0000.000NaN0.0000.0000.000NaN0.084-0.1690.0730.0000.0770.131NaNNaN-0.1690.2521.0001.000NaN0.0000.000NaN0.0000.0000.0000.000NaN1.000NaN0.000NaN0.0000.0591.000-0.1690.077-0.0350.4731.0000.0590.0380.2050.120NaN0.308NaN-0.240-0.169
ROOF_COVER0.2200.0890.2110.1980.2150.1950.1810.2650.2370.0000.1440.2190.3030.0820.0190.2620.0020.1660.1390.1090.1980.1650.1790.1910.0880.0900.2720.0000.2680.0380.0030.0130.1600.1700.2600.2620.1570.0000.0210.0591.0000.5060.2450.0640.2330.0000.0000.1540.280
ROOF_STRUCTURE0.1740.0550.2210.1380.1870.1380.1240.2650.2190.0000.1460.1700.2590.0610.0110.2580.0000.0770.0890.1170.1600.1150.1720.1360.0660.0230.2890.0000.2790.0440.0000.0100.1490.1240.2650.2580.1310.0000.0170.0380.5061.0000.2030.0600.2470.0000.0030.1460.269
STRUCTURE_CLASS0.1460.3640.1380.3070.3130.4130.3890.2590.3411.0000.0460.2840.5960.0990.0650.2470.1020.0890.3740.1030.1780.3380.2250.2980.1280.0300.1720.0960.3070.1910.0000.0390.3700.2860.1990.2470.3241.0000.1220.2050.2450.2031.0000.0840.1881.0000.0620.2290.258
ST_NUM0.0320.035-0.1460.0600.0390.0400.0210.1760.0660.1450.0620.0440.0750.000-0.112-0.058-0.0890.0710.035-0.0940.0460.028-0.1250.0510.0070.0160.049-0.0650.0670.0730.000-0.1030.0880.0210.086-0.0580.0290.055-0.0880.1200.0640.0600.0841.000-0.1490.028-0.0280.016-0.058
TT_RMS0.1950.1270.9400.1220.2070.271-0.2160.2200.135NaN0.0850.1660.2180.0540.6640.2550.9250.0840.1240.1780.1520.0410.7210.1290.0290.0500.4300.9000.4080.2940.0030.4570.1720.0550.2920.2550.105NaN0.767NaN0.2330.2470.188-0.1491.000-0.1760.2820.1250.255
YR_BUILT0.0000.000-0.1470.0000.0001.0000.1840.0000.1010.1050.0000.0090.000-0.062-0.0600.150-0.1040.0000.0000.0910.0060.000-0.1650.0001.0001.0000.000-0.1040.000-0.0450.0000.1420.0000.0000.0000.1500.0000.043-0.1610.3080.0000.0001.0000.028-0.1761.0000.0550.1600.150
YR_REMODEL0.0060.0000.2770.0060.0000.008-0.0700.021-0.023NaN0.0000.0000.0360.1040.233-0.0230.2430.0000.0000.1900.0120.0000.0660.0060.0650.0000.0080.2480.033-0.0380.0000.1660.0000.0340.013-0.0230.000NaN0.197NaN0.0000.0030.062-0.0280.2820.0551.0000.007-0.023
ZIP_CODE0.1190.1270.1230.1590.2090.239-0.2320.6940.582-0.1370.1010.0990.218-0.0110.0520.6080.0700.0670.0830.0680.1210.1180.0970.1560.0660.0410.1660.0040.140-0.1340.0030.2860.1570.0910.1270.6080.155-0.008-0.024-0.2400.1540.1460.2290.0160.1250.1600.0071.0000.608
_id0.2130.1550.2610.2180.2580.273-0.2600.7521.000-0.1250.1690.1930.2440.0180.0621.0000.1940.1760.1310.0810.2150.1660.1260.2180.1380.1050.2280.1160.211-0.1250.0040.4380.1620.0760.2551.0000.185-0.0040.034-0.1690.2800.2690.258-0.0580.2550.150-0.0230.6081.000

Missing values

2024-09-12T14:38:43.994123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-12T14:38:44.986382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-12T14:38:48.804477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idPIDCM_IDGIS_IDST_NUMST_NAMEUNIT_NUMCITYZIP_CODEBLDG_SEQNUM_BLDGSLUCLULU_DESCBLDG_TYPEOWN_OCCOWNERMAIL_ADDRESSEEMAIL_STREET_ADDRESSMAIL_CITYMAIL_STATEMAIL_ZIP_CODERES_FLOORCD_FLOORRES_UNITSCOM_UNITSRC_UNITSLAND_SFGROSS_AREALIVING_AREALAND_VALUEBLDG_VALUESFYI_VALUETOTAL_VALUEGROSS_TAXYR_BUILTYR_REMODELSTRUCTURE_CLASSROOF_STRUCTUREROOF_COVERINT_WALLEXT_FNISHEDINT_CONDEXT_CONDOVERALL_CONDBED_RMSFULL_BTHHLF_BTHKITCHENSTT_RMSBDRM_CONDBTHRM_STYLE1BTHRM_STYLE2BTHRM_STYLE3KITCHEN_TYPEKITCHEN_STYLE1KITCHEN_STYLE2KITCHEN_STYLE3HEAT_TYPEHEAT_SYSTEMAC_TYPEFIREPLACESORIENTATIONNUM_PARKINGPROP_VIEWCORNER_UNIT
01100001000NaN100001000104.0PUTNAM STNaNEAST BOSTON2128.011105R3THREE-FAM DWELLINGRE - Row EndYPASCUCCI CARLONaN195 LEXINGTON STEAST BOSTONMA2128.03.0NaNNaNNaNNaN1,1503353.02202.0197,600594,4000792,000$8,632.801900.0NaNNaNF - FlatC - CompositionN - NormalA - AsbestosA - AverageF - FairA - Average6.03.00.03.012.0NaNS - Semi-ModernS - Semi-ModernS - Semi-Modern3F - 3 Full Eat In KitchensS - Semi-ModernS - Semi-ModernS - Semi-ModernW - Ht Water/SteamNaNN - None0.0NaN3.0A - AverageNaN
12100002000NaN100002000197.0Lexington STNaNEAST BOSTON2128.011105R3THREE-FAM DWELLINGRM - Row MiddleNSEMBRANO RODERICKNaN197 LEXINGTON STEAST BOSTONMA2128.03.0NaNNaNNaNNaN1,1503047.02307.0198,500619,7000818,200$8,918.381920.02000.0NaNF - FlatC - CompositionN - NormalM - VinylA - AverageA - AverageA - Average3.03.00.03.09.0NaNM - ModernM - ModernM - Modern3F - 3 Full Eat In KitchensM - ModernM - ModernM - ModernF - Forced Hot AirNaNC - Central AC0.0NaN0.0A - AverageNaN
23100003000NaN100003000199.0Lexington STNaNEAST BOSTON2128.011105R3THREE-FAM DWELLINGRM - Row MiddleYGUERRA CHEVARRIA ANA SNaN199 LEXINGTON STEAST BOSTONMA2128.03.0NaNNaNNaNNaN1,1503392.02268.0199,100605,3000804,400$8,767.961905.01985.0NaNF - FlatC - CompositionN - NormalM - VinylA - AverageG - GoodA - Average5.03.00.03.013.0NaNM - ModernM - ModernM - Modern3F - 3 Full Eat In KitchensS - Semi-ModernS - Semi-ModernS - Semi-ModernS - Space HeatNaNN - None0.0NaN0.0A - AverageNaN
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